hypothesis examples in machine learning

Machine Learning

Artificial Intelligence

Control System

Supervised Learning

Classification, miscellaneous, related tutorials.

Interview Questions

• Send your Feedback to [email protected]

Help Others, Please Share

Learn Latest Tutorials

Transact-SQL

Reinforcement Learning

R Programming

React Native

Python Design Patterns

Python Pillow

Python Turtle

Preparation

Verbal Ability

Company Questions

Trending Technologies

Cloud Computing

Data Science

B.Tech / MCA

Data Structures

Operating System

Computer Network

Compiler Design

Computer Organization

Discrete Mathematics

Ethical Hacking

Computer Graphics

Software Engineering

Web Technology

Cyber Security

C Programming

Data Mining

Data Warehouse

Best Guesses: Understanding The Hypothesis in Machine Learning

• February 22, 2024
• General , Supervised Learning , Unsupervised Learning

Machine learning is a vast and complex field that has inherited many terms from other places all over the mathematical domain.

It can sometimes be challenging to get your head around all the different terminologies, never mind trying to understand how everything comes together.

In this blog post, we will focus on one particular concept: the hypothesis.

While you may think this is simple, there is a little caveat regarding machine learning.

The statistics side and the learning side.

Don’t worry; we’ll do a full breakdown below.

You’ll learn the following:

What Is a Hypothesis in Machine Learning?

• Is This any different than the hypothesis in statistics?
• What is the difference between the alternative hypothesis and the null?
• Why do we restrict hypothesis space in artificial intelligence?
• Example code performing hypothesis testing in machine learning

In machine learning, the term ‘hypothesis’ can refer to two things.

First, it can refer to the hypothesis space, the set of all possible training examples that could be used to predict or answer a new instance.

Second, it can refer to the traditional null and alternative hypotheses from statistics.

Since machine learning works so closely with statistics, 90% of the time, when someone is referencing the hypothesis, they’re referencing hypothesis tests from statistics.

Is This Any Different Than The Hypothesis In Statistics?

In statistics, the hypothesis is an assumption made about a population parameter.

The statistician’s goal is to prove it true or disprove it.

This will take the form of two different hypotheses, one called the null, and one called the alternative.

Usually, you’ll establish your null hypothesis as an assumption that it equals some value.

For example, in Welch’s T-Test Of Unequal Variance, our null hypothesis is that the two means we are testing (population parameter) are equal.

This means our null hypothesis is that the two population means are the same.

We run our statistical tests, and if our p-value is significant (very low), we reject the null hypothesis.

This would mean that their population means are unequal for the two samples you are testing.

Usually, statisticians will use the significance level of .05 (a 5% risk of being wrong) when deciding what to use as the p-value cut-off.

What Is The Difference Between The Alternative Hypothesis And The Null?

The null hypothesis is our default assumption, which we are trying to prove correct.

The alternate hypothesis is usually the opposite of our null and is much broader in scope.

For most statistical tests, the null and alternative hypotheses are already defined.

You are then just trying to find “significant” evidence we can use to reject our null hypothesis.

These two hypotheses are easy to spot by their specific notation. The null hypothesis is usually denoted by H₀, while H₁ denotes the alternative hypothesis.

Example Code Performing Hypothesis Testing In Machine Learning

Since there are many different hypothesis tests in machine learning and data science, we will focus on one of my favorites.

This test is Welch’s T-Test Of Unequal Variance, where we are trying to determine if the population means of these two samples are different.

There are a couple of assumptions for this test, but we will ignore those for now and show the code.

You can read more about this here in our other post, Welch’s T-Test of Unequal Variance .

We see that our p-value is very low, and we reject the null hypothesis.

What Is The Difference Between The Biased And Unbiased Hypothesis Spaces?

The difference between the Biased and Unbiased hypothesis space is the number of possible training examples your algorithm has to predict.

The unbiased space has all of them, and the biased space only has the training examples you’ve supplied.

Since neither of these is optimal (one is too small, one is much too big), your algorithm creates generalized rules (inductive learning) to be able to handle examples it hasn’t seen before.

Here’s an example of each:

Example of The Biased Hypothesis Space In Machine Learning

The Biased Hypothesis space in machine learning is a biased subspace where your algorithm does not consider all training examples to make predictions.

This is easiest to see with an example.

Let’s say you have the following data:

Happy  and  Sunny  and  Stomach Full  = True

Whenever your algorithm sees those three together in the biased hypothesis space, it’ll automatically default to true.

This means when your algorithm sees:

Sad  and  Sunny  And  Stomach Full  = False

It’ll automatically default to False since it didn’t appear in our subspace.

This is a greedy approach, but it has some practical applications.

Example of the Unbiased Hypothesis Space In Machine Learning

The unbiased hypothesis space is a space where all combinations are stored.

We can use re-use our example above:

This would start to breakdown as

Happy  = True

Happy  and  Sunny  = True

Happy  and  Stomach Full  = True

Let’s say you have four options for each of the three choices.

This would mean our subspace would need 2^12 instances (4096) just for our little three-word problem.

This is practically impossible; the space would become huge.

So while it would be highly accurate, this has no scalability.

More reading on this idea can be found in our post, Inductive Bias In Machine Learning .

Why Do We Restrict Hypothesis Space In Artificial Intelligence?

We have to restrict the hypothesis space in machine learning. Without any restrictions, our domain becomes much too large, and we lose any form of scalability.

This is why our algorithm creates rules to handle examples that are seen in production. 

This gives our algorithms a generalized approach that will be able to handle all new examples that are in the same format.

Other Quick Machine Learning Tutorials

At EML, we have a ton of cool data science tutorials that break things down so anyone can understand them.

Below we’ve listed a few that are similar to this guide:

• Instance-Based Learning in Machine Learning
• Types of Data For Machine Learning
• Verbose in Machine Learning
• Generalization In Machine Learning
• Epoch In Machine Learning
• Inductive Bias in Machine Learning
• Understanding The Hypothesis In Machine Learning
• Zip Codes In Machine Learning
• get_dummies() in Machine Learning
• Bootstrapping In Machine Learning
• X and Y in Machine Learning
• F1 Score in Machine Learning
• Recent Posts

• How to Write a Graph Title [Boost Your Data Visualization Skills] - September 10, 2024
• Are Software Engineers Unhappy? [Discover How to Reignite Your Passion] - September 10, 2024
• Understanding Functions: Simplifying Complex Concepts [Unlock the Mystery] - September 10, 2024

Hold on, while it loads...

Evaluating Hypotheses in Machine Learning: A Comprehensive Guide

Learn how to evaluate hypotheses in machine learning, including types of hypotheses, evaluation metrics, and common pitfalls to avoid. Improve your ML model's performance with this in-depth guide.

Create an image featuring JavaScript code snippets and interview-related icons or graphics. Use a color scheme of yellows and blues. Include the title '7 Essential JavaScript Interview Questions for Freshers'.

Introduction

Machine learning is a crucial aspect of artificial intelligence that enables machines to learn from data and make predictions or decisions. The process of machine learning involves training a model on a dataset, and then using that model to make predictions on new, unseen data. However, before deploying a machine learning model, it is essential to evaluate its performance to ensure that it is accurate and reliable. One crucial step in this evaluation process is hypothesis testing.

In this blog post, we will delve into the world of hypothesis testing in machine learning, exploring what hypotheses are, why they are essential, and how to evaluate them. We will also discuss the different types of hypotheses, common pitfalls to avoid, and best practices for hypothesis testing.

What are Hypotheses in Machine Learning?

In machine learning, a hypothesis is a statement that proposes a possible explanation for a phenomenon or a problem. It is a conjecture that is made about a population parameter, and it is used as a basis for further investigation. In the context of machine learning, hypotheses are used to define the problem that we are trying to solve.

For example, let's say we are building a machine learning model to predict the prices of houses based on their features, such as the number of bedrooms, square footage, and location. A possible hypothesis could be: "The price of a house is directly proportional to its square footage." This hypothesis proposes a possible relationship between the price of a house and its square footage.

Why are Hypotheses Essential in Machine Learning?

Hypotheses are essential in machine learning because they provide a framework for understanding the problem that we are trying to solve. They help us to identify the key variables that are relevant to the problem, and they provide a basis for evaluating the performance of our machine learning model.

Without a clear hypothesis, it is difficult to develop an effective machine learning model. A hypothesis helps us to:

• Identify the key variables that are relevant to the problem
• Develop a clear understanding of the problem that we are trying to solve
• Evaluate the performance of our machine learning model
• Refine our model and improve its accuracy

Types of Hypotheses in Machine Learning

There are two main types of hypotheses in machine learning: null hypotheses and alternative hypotheses.

Null Hypothesis

A null hypothesis is a hypothesis that proposes that there is no significant difference or relationship between variables. It is a hypothesis of no effect or no difference. For example, let's say we are building a machine learning model to predict the prices of houses based on their features. A null hypothesis could be: "There is no significant relationship between the price of a house and its square footage."

Alternative Hypothesis

An alternative hypothesis is a hypothesis that proposes that there is a significant difference or relationship between variables. It is a hypothesis of an effect or a difference. For example, let's say we are building a machine learning model to predict the prices of houses based on their features. An alternative hypothesis could be: "There is a significant positive relationship between the price of a house and its square footage."

Evaluating Hypotheses in Machine Learning

Evaluating hypotheses in machine learning involves testing the null hypothesis against the alternative hypothesis. This is typically done using statistical methods, such as t-tests, ANOVA, and regression analysis.

Here are the general steps involved in evaluating hypotheses in machine learning:

• Formulate the null and alternative hypotheses : Clearly define the null and alternative hypotheses that you want to test.
• Collect and prepare the data : Collect the data that you will use to test the hypotheses. Ensure that the data is clean, relevant, and representative of the population.
• Choose a statistical method : Select a suitable statistical method to test the hypotheses. This could be a t-test, ANOVA, regression analysis, or another method.
• Test the hypotheses : Use the chosen statistical method to test the null hypothesis against the alternative hypothesis.
• Interpret the results : Interpret the results of the hypothesis test. If the null hypothesis is rejected, it suggests that there is a significant relationship between the variables. If the null hypothesis is not rejected, it suggests that there is no significant relationship between the variables.

Common Pitfalls to Avoid in Hypothesis Testing

Here are some common pitfalls to avoid in hypothesis testing:

• Overfitting : Overfitting occurs when a model is too complex and performs well on the training data but poorly on new, unseen data. To avoid overfitting, use techniques such as regularization, early stopping, and cross-validation.
• Underfitting : Underfitting occurs when a model is too simple and fails to capture the underlying patterns in the data. To avoid underfitting, use techniques such as feature engineering, hyperparameter tuning, and model selection.
• Data leakage : Data leakage occurs when the model is trained on data that it will also be tested on. To avoid data leakage, use techniques such as cross-validation and walk-forward optimization.
• P-hacking : P-hacking occurs when a researcher selectively reports the results of multiple hypothesis tests to find a significant result. To avoid p-hacking, use techniques such as preregistration and replication.

Best Practices for Hypothesis Testing in Machine Learning

Here are some best practices for hypothesis testing in machine learning:

• Clearly define the hypotheses : Clearly define the null and alternative hypotheses that you want to test.
• Use a suitable statistical method : Choose a suitable statistical method to test the hypotheses.
• Use cross-validation : Use cross-validation to evaluate the performance of the model on unseen data.
• Avoid overfitting and underfitting : Use techniques such as regularization, early stopping, and feature engineering to avoid overfitting and underfitting.
• Document the results : Document the results of the hypothesis test, including the statistical method used, the results, and any conclusions drawn.

Evaluating hypotheses is a crucial step in machine learning that helps us to understand the problem that we are trying to solve and to evaluate the performance of our machine learning model. By following the best practices outlined in this blog post, you can ensure that your hypothesis testing is rigorous, reliable, and effective.

Remember to clearly define the null and alternative hypotheses, choose a suitable statistical method, and avoid common pitfalls such as overfitting, underfitting, data leakage, and p-hacking. By doing so, you can develop machine learning models that are accurate, reliable, and effective.

• [1] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R. Springer.
• [2] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
• [3] Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann.

I hope this helps! Let me know if you need any further assistance.

Hypothesis Testing in Machine Learning

The process of hypothesis testing is to draw inferences or some conclusion about the overall population or data by conducting some statistical tests on a sample. The same inferences are drawn for different machine learning models through T-test which I will discuss in this tutorial.

For drawing some inferences, we have to make some assumptions that lead to two terms that are used in the hypothesis testing.

Null hypothesis: It is regarding the assumption that there is no anomaly pattern or believing according to the assumption made.

Alternate hypothesis: Contrary to the null hypothesis, it shows that observation is the result of real effect.

It can also be said as evidence or level of significance for the null hypothesis or in machine learning algorithms. It’s the significance of the predictors towards the target.

Generally, we select the level of significance by 5 %, but it is also a topic of discussion for some cases. If you have a strong prior knowledge about your data functionality, you can decide the level of significance.

On the contrary of that if the p-value is less than 0.05 in a machine learning model against an independent variable, then the variable is considered which means there is heterogeneous behavior with the target which is useful and can be learned by the machine learning algorithms.

The steps involved in the hypothesis testing are as follow:

Assume a null hypothesis, usually in machine learning algorithms we consider that there is no anomaly between the target and independent variable.

Collect a sample

Calculate test statistics

Decide either to accept or reject the null hypothesis

Calculating test or T statistics

For Calculating T statistics, we create a scenario.

Suppose there is a shipping container making company which claims that each container is 1000 kg in weight not less, not more. Well, such claims look shady, so we proceed with gathering data and creating a sample.

After gathering a sample of 30 containers, we found that the average weight of the container is 990 kg and showing a standard deviation of 12.5 kg.

So calculating test statistics:

T = (Mean - Claim)/ (Standard deviation / Sample Size^(1/2))

Which is -4.3818 after putting all the numbers.

Now we calculate t value for 0.05 significance and degree of freedom.

Note: Degree of Freedom = Sample Size - 1

From T table the value will be -1.699.

After comparison, we can see that the generated statistics are less than the statistics of the desired level of significance. So we can reject the claim made.

You can calculate the t value using stats.t.ppf() function of stats class of scipy library.

As hypothesis testing is done on a sample of data rather than the entire population due to the unavailability of the resources in terms of data. Due to inferences are drawn on sample data the hypothesis testing can lead to errors, which can be classified into two parts:

Type I Error: In this error, we reject the null hypothesis when it is true.

Type II Error: In this error, we accept the null hypothesis when it is false.

Other Approaches

A lot of different approaches are present to hypothesis testing of two models like creating two models on the features available with us. One model comprises all the features and the other with one less. So we can test the significance of individual features. However feature inter-dependency affect such simple methods.

In regression problems, we generally follow the rule of P value, the feature which violates the significance level are removed, thus iteratively improving the model.

Different approaches are present for each algorithm to test the hypothesis on different features.

If you would like to learn more about Bayesian inferences fundamentals, take DataCamp's Fundamentals of Bayesian Data Analysis in R course.

Check out our Machine Learning Basics tutorial.

Learn more about Machine Learning

.css-1531qan{-webkit-text-decoration:none;text-decoration:none;color:inherit;} Understanding Machine Learning

Machine learning with tree-based models in python, machine learning for time series data in python, hyperparameter optimization in machine learning models.

Machine Learning in R for beginners

Karlijn Willems

Probability Distributions in Python Tutorial

DataCamp Team

An Introduction to Statistical Machine Learning

Joanne Xiong

Common Data Science Pitfalls & How to Avoid them!

Getting started with machine learning in python.

George Boorman

Hypothesis Testing with Python: Step by step hands-on tutorial with practical examples

Ece Işık Polat

Towards Data Science

Hypotheses are claims, and we can use statistics to prove or disprove them. At this point, hypothesis testing structures the problems so that we can use statistical evidence to test these claims. So we can check whether or not the claim is valid.

In this article, I want to show hypothesis testing with Python on several questions step-by-step. But before, let me explain the hypothesis testing process briefly. If you wish, you can move to the questions directly.

1. Defining Hypotheses

First of all, we should understand which scientific question we are looking for an answer to, and it should be formulated in the form of the Null Hypothesis (H₀) and the Alternative Hypothesis (H₁ or Hₐ). Please remember that H₀ and H₁ must be mutually exclusive, and H ₁ shouldn’t contain equality:

• H₀: μ=x, H₁: μ≠x
• H₀: μ≤x, H₁: μ>x
• H₀: μ≥x, H₁: μ<x

2. Assumption Check

To decide whether to use the parametric or nonparametric version of the test, we should check the specific requirements listed below:

• Observations in each sample are independent and identically distributed (IID).
• Observations in each sample are normally distributed.
• Observations in each sample have the same variance.

3. Selecting the Proper Test

Then we select the appropriate test to be used. When choosing the proper test, it is essential to analyze how many groups are being compared and whether the data are paired or not. To determine whether the data is matched, it is necessary to consider whether the data was collected from the same individuals. Accordingly, you can decide on the appropriate test using the chart below.

4. Decision and Conclusion

After performing the hypothesis testing, we obtain a related p -value that shows the significance of the test.

If the p -value is smaller than the alpha (the significance level), in other words, there is enough evidence to prove H₀ is not valid; you can reject H₀. Otherwise, you fail to reject H₀. Please remember that rejecting H₀ validates H₁. However, failing to reject H₀ does not mean H₀ is valid, nor does it mean H₁ is wrong.

Now we are ready to start the code part.

You can visit https://github.com/eceisik/eip/blob/main/hypothesis_testing_examples.ipynb to see the full implementation.

Q1. t-test independent

A university professor gave online lectures instead of face-to-face classes due to Covid-19. Later, he uploaded recorded lectures to the cloud for students who followed the course asynchronously (those who did not attend the lesson but later watched the records). However, he believes that the students who attend class at the class time and participate in the process are more successful. Therefore, he recorded the average grades of the students at the end of the semester. The data is below.

synchronous = [94. , 84.9, 82.6, 69.5, 80.1, 79.6, 81.4, 77.8, 81.7, 78.8, 73.2, 87.9, 87.9, 93.5, 82.3, 79.3, 78.3, 71.6, 88.6, 74.6, 74.1, 80.6] asynchronous = [77.1, 71.7, 91. , 72.2, 74.8, 85.1, 67.6, 69.9, 75.3, 71.7, 65.7, 72.6, 71.5, 78.2]

Conduct the hypothesis testing to check whether the professor’s belief is statistically significant by using a 0.05 significance level to evaluate the null and alternative hypotheses. Before doing hypothesis testing, check the related assumptions. Comment on the results.

1. Defining Hypothesis

Since the grades are obtained from the different individuals, the data is unpaired.

H₀: μₛ≤μₐ H₁ : μₛ>μₐ

H₀: The data is normally distributed. H₁: The data is not normally distributed. Assume that α=0.05. If the p -value is >0.05, it can be said that data is normally distributed.

For checking normality, I used Shapiro-Wilk’s W test which is generally preferred for smaller samples however there are other options like Kolmogorov-Smirnov and D’Agostino and Pearson’s test. Please visit https://docs.scipy.org/doc/scipy/reference/stats.html for more information.

H₀: The variances of the samples are the same. H₁: The variances of the samples are different.

It tests the null hypothesis that the population variances are equal (called homogeneity of variance or homoscedasticity). Suppose the resulting p -value of Levene’s test is less than the significance level (typically 0.05). In that case, the obtained differences in sample variances are unlikely to have occurred based on random sampling from a population with equal variances.

For checking variance homogeneity, I preferred Levene’s test but you can also check Bartlett’s test from here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bartlett.html#scipy.stats.bartlett

Since assumptions are satisfied, we can perform the parametric version of the test for 2 groups and unpaired data.

At this significance level, there is enough evidence to conclude that the average grade of the students who follow the course synchronously is higher than the students who follow the course asynchronously.

A pediatrician wants to see the effect of formula consumption on the average monthly weight gain (in gr) of babies. For this reason, she collected data from three different groups. The first group is exclusively breastfed children (receives only breast milk), the second group is children who are fed with only formula and the last group is both formula and breastfed children. These data are as below.

only_breast =[794.1, 716.9, 993. , 724.7, 760.9, 908.2, 659.3 , 690.8, 768.7, 717.3 , 630.7, 729.5, 714.1, 810.3, 583.5, 679.9, 865.1]

only_formula =[ 898.8, 881.2, 940.2, 966.2, 957.5, 1061.7, 1046.2, 980.4, 895.6, 919.7, 1074.1, 952.5, 796.3, 859.6, 871.1 , 1047.5, 919.1 , 1160.5, 996.9]

both =[976.4, 656.4, 861.2, 706.8, 718.5, 717.1, 759.8, 894.6, 867.6, 805.6, 765.4, 800.3, 789.9, 875.3, 740. , 799.4, 790.3, 795.2 , 823.6, 818.7, 926.8, 791.7, 948.3]

According to this information, conduct the hypothesis testing to check whether there is a difference between the average monthly gain of these three groups by using a 0.05 significance level. If there is a significant difference, perform further analysis to find what caused the difference. Before doing hypothesis testing, check the related assumptions.

H₀: μ₁=μ₂=μ₃ or The mean of the samples is the same. H₁: At least one of them is different.

H₀: The data is normally distributed. H₁: The data is not normally distributed.

Since assumptions are satisfied, we can perform the parametric version of the test for more than 2 groups and unpaired data.

At this significance level, it can be concluded that at least one of the groups has a different average monthly weight gain. To find which group or groups cause the difference, we need to perform a posthoc test/pairwise comparison as below.

Note: To avoid family-wise p -value inflation, I used Bonferroni adjustment. You can see your other alternative from here: https://scikit-posthocs.readthedocs.io/en/latest/generated/scikit_posthocs.posthoc_ttest/

At this significance level, it can be concluded that:

“only breast” is different than “only formula” “only formula” is different than both “only breast” and “both” “both” is different than “only formula”

Q3. Mann Whitney U

A human resource specialist working in a technology company is interested in the overwork time of different teams. To investigate whether there is a difference between overtime of the software development team and the test team, she selected 17 employees randomly in each of the two teams and recorded their weekly average overwork time in terms of an hour. The data is below.

test_team =[6.2, 7.1, 1.5, 2,3 , 2, 1.5, 6.1, 2.4, 2.3, 12.4, 1.8, 5.3, 3.1, 9.4, 2.3, 4.1] developer_team =[2.3, 2.1, 1.4, 2.0, 8.7, 2.2, 3.1, 4.2, 3.6, 2.5, 3.1, 6.2, 12.1, 3.9, 2.2, 1.2 ,3.4]

According to this information, conduct the hypothesis testing to check whether there is a difference between the overwork time of two teams by using a 0.05 significance level. Before doing hypothesis testing, check the related assumptions.

H₀: μ₁≤μ₂ H₁ : μ₁>μ₂

There are two groups, and data is collected from different individuals, so it is not paired. However, the normality assumption is not satisfied; therefore, we need to use the nonparametric version of 2 group comparison for unpaired data: the Mann-Whitney U Test.

At this significance level, it can be said that there is no statistically significant difference between the average overwork time of the two teams.

Q4. Kruskal-Wallis

An e-commerce company regularly advertises on YouTube, Instagram, and Facebook for its campaigns. However, the new manager was curious about if there was any difference between the number of customers attracted by these platforms. Therefore, she started to use Adjust, an application that allows you to find out where your users come from. The daily numbers reported from Adjust for each platform are as below.

Youtube =[1913, 1879, 1939, 2146, 2040, 2127, 2122, 2156, 2036, 1974, 1956, 2146, 2151, 1943, 2125]

Instagram = [2305., 2355., 2203., 2231., 2185., 2420., 2386., 2410., 2340., 2349., 2241., 2396., 2244., 2267., 2281.]

Facebook = [2133., 2522., 2124., 2551., 2293., 2367., 2460., 2311., 2178., 2113., 2048., 2443., 2265., 2095., 2528.]

According to this information, conduct the hypothesis testing to check whether there is a difference between the average customer acquisition of these three platforms using a 0.05 significance level. If there is a significant difference, perform further analysis to find that caused the difference. Before doing hypothesis testing, check the related assumptions.

The normality and variance homogeneity assumptions are not satisfied, therefore we need to use the nonparametric version of ANOVA for unpaired data (the data is collected from different sources).

At this significance level, at least one of the average customer acquisition number is different. Note: Since the data is not normal, the nonparametric version of posthoc test is used.

The average number of customers coming from YouTube is different than the other (actually smaller than the others).

Q5. t-test dependent

The University Health Center diagnosed eighteen students with high cholesterol in the previous semester. Healthcare personnel told these patients about the dangers of high cholesterol and prescribed a diet program. One month later, the patients came for control, and their cholesterol level was reexamined. Test whether there is a difference in the cholesterol levels of the patients.

According to this information, conduct the hypothesis testing to check whether there is a decrease in the cholesterol levels of the patients after the diet by using a 0.05 significance level. Before doing hypothesis testing, check the related assumptions. Comment on the results

test_results_before_diet =[224, 235, 223, 253, 253, 224, 244, 225, 259, 220, 242, 240, 239, 229, 276, 254, 237, 227] test_results_after_diet =[198, 195, 213, 190, 246, 206, 225, 199, 214, 210, 188, 205, 200, 220, 190, 199, 191, 218]

H₀: μd>=0 or The true mean difference is equal to or bigger than zero. H₁: μd<0 or The true mean difference is smaller than zero.

• The dependent variable must be continuous (interval/ratio) • The observations are independent of one another. • The dependent variable should be approximately normally distributed.

The data is paired since data is collected from the same individuals and assumptions are satisfied, then we can use the dependent t-test.

At this significance level, there is enough evidence to conclude mean cholesterol level of patients has decreased after the diet.

Q6. Wilcoxon signed-rank test

A venture capitalist wanted to invest in a startup that provides data compression without any loss in quality, but there are two competitors: PiedPiper and EndFrame. Initially, she believed the performance of the EndFrame could be better but still wanted to test it before the investment. Then, she gave the same files to each company to compress and recorded their performance scores. The data is below.

piedpiper =[4.57, 4.55, 5.47, 4.67, 5.41, 5.55, 5.53, 5.63, 3.86, 3.97, 5.44, 3.93, 5.31, 5.17, 4.39, 4.28, 5.25] endframe = [4.27, 3.93, 4.01, 4.07, 3.87, 4. , 4. , 3.72, 4.16, 4.1 , 3.9 , 3.97, 4.08, 3.96, 3.96, 3.77, 4.09]

According to this information, conduct the related hypothesis testing by using a 0.05 significance level. Before doing hypothesis testing, check the related assumptions. Comment on the results.

Since the performance scores are obtained from the same files, the data is paired.

The normality assumption is not satisfied; therefore, we need to use the nonparametric version of the paired test, namely the Wilcoxon Signed Rank test.

At this significance level, there is enough evidence to conclude that the performance of the PiedPaper is better than the EndFrame.

Q7. Friedman Chi-Square

A researcher was curious about whether there is a difference between the methodology she developed, C, and baseline methods A and B in terms of performance. Therefore, she decided to design different experiments and recorded the achieved accuracy by each method. The below table shows the achieved accuracy on test sets by each method. Please note that the same train and test sets were used for each method.

According to this information, conduct the hypothesis testing to check whether there is a difference between the performance of the methods by using a 0.05 significance level. If there is a significant difference, perform further analysis to find which one caused the difference. Before doing hypothesis testing, check the related assumptions. Comment on the results.

There are three groups, but the normality assumption is violated. So, we need to use the nonparametric version of ANOVA for paired data since the accuracy scores are obtained from the same test sets.

At this significance level, at least one of the methods has a different performance.

Note: Since the data is not normal, the nonparametric version of the posthoc test is used.

Method C outperformed others and achieved better accuracy scores than the others.

Q8. The goodness of Fit (Bonus :)

An analyst of a financial investment company is curious about the relationship between gender and risk appetite. A random sample was taken of 660 customers from the database. The customers in the sample were classified according to their gender and their risk appetite. The result is given in the following table.

Test the hypothesis that the risk appetite of the customers in this company is independent of their gender. Use α = 0.01 .

H₀: Gender and risk appetite are independent. H₁: Gender and risk appetite are dependent.

2. Selecting the Proper Test and Assumption Check

chi2 test should be used for this question. This test is known as the goodness-of-fit test. It implies that if the observed data are very close to the expected data. The assumption of this test every Ei ≥ 5 (in at least 80% of the cells) is satisfied.

3. Decision and Conclusion

Since the p-value is larger than α=0.01 ( or calculated statistic=7.14 is smaller than the critical statistic=13.28) → Fail to Reject H₀. At this significance level, it can be concluded that gender and risk appetite are independent.

Written by Ece Işık Polat

ML Researcher at Middle East Technical University https://www.linkedin.com/in/eceisikpolat/ https://github.com/eceisik

Text to speech

Hypothesis in Machine Learning: Comprehensive Overview(2021)

Introduction

Supervised machine learning (ML) is regularly portrayed as the issue of approximating an objective capacity that maps inputs to outputs. This portrayal is described as looking through and assessing competitor hypothesis from hypothesis spaces. 

The conversation of hypothesis in machine learning can be confused for a novice, particularly when “hypothesis” has a discrete, but correlated significance in statistics and all the more comprehensively in science.

Hypothesis Space (H)

The hypothesis space utilized by an ML system is the arrangement of all hypotheses that may be returned by it. It is ordinarily characterized by a Hypothesis Language, conceivably related to a Language Bias. 

Many ML algorithms depend on some sort of search methodology: given a set of perceptions and a space of all potential hypotheses that may be thought in the hypothesis space. They see in this space for those hypotheses that adequately furnish the data or are ideal concerning some other quality standard.

ML can be portrayed as the need to utilize accessible data objects to discover a function that most reliable maps inputs to output, alluded to as function estimate, where we surmised an anonymous objective function that can most reliably map inputs to outputs on all expected perceptions from the difficult domain. An illustration of a model that approximates the performs mappings and target function of inputs to outputs is known as hypothesis testing in machine learning.

The hypothesis in machine learning of all potential hypothesis that you are looking over, paying little mind to their structure. For the wellbeing of accommodation, the hypothesis class is normally compelled to be just each sort of function or model in turn, since learning techniques regularly just work on each type at a time. This doesn’t need to be the situation, however:

• Hypothesis classes don’t need to comprise just one kind of function. If you’re looking through exponential, quadratic, and overall linear functions, those are what your joined hypothesis class contains.
• Hypothesis classes additionally don’t need to comprise of just straightforward functions. If you figure out how to look over all piecewise-tanh2 functions, those functions are what your hypothesis class incorporates.

The enormous trade-off is that the bigger your hypothesis class in   machine learning, the better the best hypothesis models the basic genuine function, yet the harder it is to locate that best hypothesis. This is identified with the bias-variance trade-off.

• Hypothesis (h)

A hypothesis function in machine learning is best describes the target. The hypothesis that an algorithm would concoct relies on the data and relies on the bias and restrictions that we have forced on the data.

The hypothesis formula in machine learning:

• y  is range
• m  changes in y divided by change in x
• x  is domain
• b  is intercept

The purpose of restricting hypothesis space in machine learning is so that these can fit well with the general data that is needed by the user. It checks the reality or deception of observations or inputs and examinations them appropriately. Subsequently, it is extremely helpful and it plays out the valuable function of mapping all the inputs till they come out as outputs. Consequently, the target functions are deliberately examined and restricted dependent on the outcomes (regardless of whether they are free of bias), in ML.

The hypothesis in machine learning space and inductive bias in machine learning is that the hypothesis space is a collection of valid Hypothesis, for example, every single desirable function, on the opposite side the inductive bias (otherwise called learning bias) of a learning algorithm is the series of expectations that the learner uses to foresee outputs of given sources of inputs that it has not experienced. Regression and Classification are a kind of realizing which relies upon continuous-valued and discrete-valued sequentially. This sort of issues (learnings) is called inductive learning issues since we distinguish a function by inducting it on data.

In the Maximum a Posteriori or MAP hypothesis in machine learning, enhancement gives a Bayesian probability structure to fitting model parameters to training data and another option and sibling may be a more normal Maximum Likelihood Estimation system. MAP learning chooses a solitary in all probability theory given the data. The hypothesis in machine learning earlier is as yet utilized and the technique is regularly more manageable than full Bayesian learning. 

Bayesian techniques can be utilized to decide the most plausible hypothesis in machine learning given the data the MAP hypothesis. This is the ideal hypothesis as no other hypothesis is more probable.

Hypothesis in machine learning or ML the applicant model that approximates a target function for mapping instances of inputs to outputs.

Hypothesis in statistics probabilistic clarification about the presence of a connection between observations. 

Hypothesis in science is a temporary clarification that fits the proof and can be disproved or confirmed. We can see that a hypothesis in machine learning draws upon the meaning of the hypothesis all the more extensively in science.

There are no right or wrong ways of learning AI and ML technologies – the more, the better! These valuable resources can be the starting point for your journey on how to learn Artificial Intelligence and Machine Learning. Do pursuing AI and ML interest you? If you want to step into the world of emerging tech, you can accelerate your career with this  Machine Learning And AI Courses   by Jigsaw Academy.

• XGBoost Algorithm: An Easy Overview For 2021

Fill in the details to know more

PEOPLE ALSO READ

Related Articles

From The Eyes Of Emerging Technologies: IPL Through The Ages

April 29, 2023

Personalized Teaching with AI: Revolutionizing Traditional Teaching Methods

April 28, 2023

Metaverse: The Virtual Universe and its impact on the World of Finance

April 13, 2023

Artificial Intelligence – Learning To Manage The Mind Created By The Human Mind!

March 22, 2023

Wake Up to the Importance of Sleep: Celebrating World Sleep Day!

March 18, 2023

Operations Management and AI: How Do They Work?

March 15, 2023

How Does BYOP(Bring Your Own Project) Help In Building Your Portfolio?

What Are the Ethics in Artificial Intelligence (AI)?

November 25, 2022

What is Epoch in Machine Learning?| UNext

November 24, 2022

The Impact Of Artificial Intelligence (AI) in Cloud Computing

November 18, 2022

Role of Artificial Intelligence and Machine Learning in Supply Chain Management 

November 11, 2022

Best Python Libraries for Machine Learning in 2022

November 7, 2022

Are you ready to build your own career?

Query? Ask Us

Enter Your Details ×

Evaluating Hypotheses: Estimating hypotheses Accuracy

For estimating hypothesis accuracy, statistical methods are applied. In this blog, we’ll have a look at evaluating hypotheses and estimating it’s accuracy. 

Evaluating hypotheses: 

Whenever you form a hypothesis for a given training data set, for example, you came up with a hypothesis for the EnjoySport example where the attributes of the instances decide if a person will be able to enjoy their favorite sport or not. 

Now to test or evaluate how accurate the considered hypothesis is we use different statistical measures. Evaluating hypotheses is an important step in training the model. 

To evaluate the hypotheses precisely focus on these points: 

When statistical methods are applied to estimate hypotheses, 

• First, how well does this estimate the accuracy of a hypothesis across additional examples, given the observed accuracy of a hypothesis over a limited sample of data?
• Second, how likely is it that if one theory outperforms another across a set of data, it is more accurate in general?
• Third, what is the best strategy to use limited data to both learn and measure the accuracy of a hypothesis?

Motivation: 

There are instances where the accuracy of the entire model plays a huge role in the model is adopted or not. For example, consider using a training model for Medical treatment. We need to have a high accuracy so as to depend on the information the model provides. 

When we need to learn a hypothesis and estimate its future accuracy based on a small collection of data, we face two major challenges:

Bias in the estimation

There is a bias in the estimation. Initially, the observed accuracy of the learned hypothesis over training instances is a poor predictor of its accuracy over future cases.

Because the learned hypothesis was generated from previous instances, future examples will likely yield a skewed estimate of hypothesis correctness.

Estimation variability.  

Second, depending on the nature of the particular set of test examples, even if the hypothesis accuracy is tested over an unbiased set of test instances independent of the training examples, the measurement accuracy can still differ from the true accuracy. 

The anticipated variance increases as the number of test examples decreases.

When evaluating a taught hypothesis, we want to know how accurate it will be at classifying future instances.

Also, to be aware of the likely mistake in the accuracy estimate. There is an X-dimensional space of conceivable scenarios. We presume that different instances of X will be met at different times. 

Assume there is some unknown probability distribution D that describes the likelihood of encountering each instance in X. This is a convenient method to model this.

A trainer draws each instance separately, according to the distribution D, and then passes the instance x together with its correct target value f (x) to the learner as training examples of the target function f.

The following two questions are of particular relevance to us in this context, 

• What is the best estimate of the accuracy of h over future instances taken from the same distribution, given a hypothesis h and a data sample containing n examples picked at random according to the distribution D?
• What is the margin of error in this estimate of accuracy?

True Error and Sample Error: 

We must distinguish between two concepts of accuracy or, to put it another way, error. One is the hypothesis’s error rate based on the available data sample. 

The hypothesis’ error rate over the complete unknown distribution D of examples is the other. These will be referred to as the sampling error and real error, respectively.

The fraction of S that a hypothesis misclassifies is the sampling error of a hypothesis with respect to some sample S of examples selected from X.

Sample Error:

It is denoted by error s (h) of hypothesis h with respect to target function f and data sample S is 

Where n is the number of examples in S, and the quantity  is 1 if f(x) != h(x), and 0 otherwise. 

True Error: 

It is denoted by error D (h) of hypothesis h with respect to target function f and distribution D, which is the probability that h will misclassify an instance drawn at random according to D.

Confidence Intervals for Discrete-Valued Hypotheses:

“How accurate are error s (h) estimates of error D (h)?” – in the case of a discrete-valued hypothesis (h).

To estimate the true error for a discrete-valued hypothesis h based on its observed sample error over a sample S, where

• According to the probability distribution D, the sample S contains n samples drawn independently of one another and of h. 
• Over these n occurrences, hypothesis h commits r mistakes error s (h) = r/n

Under these circumstances, statistical theory permits us to state the following:

• If no additional information is available, the most likely value of error D (h) is error s (h).
• The genuine error error D (h) lies in the interval with approximately 95% probability.

A more precise rule of thumb is that the approximation described above works well when

• Trending Categories

• Selected Reading
• UPSC IAS Exams Notes
• Developer's Best Practices
• Questions and Answers
• Effective Resume Writing
• HR Interview Questions
• Computer Glossary

What is hypothesis in Machine Learning?

The hypothesis is a word that is frequently used in Machine Learning and data science initiatives. As we all know, machine learning is one of the most powerful technologies in the world, allowing us to anticipate outcomes based on previous experiences. Moreover, data scientists and ML specialists undertake experiments with the goal of solving an issue. These ML experts and data scientists make an initial guess on how to solve the challenge.

What is a Hypothesis?

A hypothesis is a conjecture or proposed explanation that is based on insufficient facts or assumptions. It is only a conjecture based on certain known facts that have yet to be confirmed. A good hypothesis is tested and yields either true or erroneous outcomes.

Let's look at an example to better grasp the hypothesis. According to some scientists, ultraviolet (UV) light can harm the eyes and induce blindness.

In this case, a scientist just states that UV rays are hazardous to the eyes, but people presume they can lead to blindness. Yet, it is conceivable that it will not be achievable. As a result, these kinds of assumptions are referred to as hypotheses.

Defining Hypothesis in Machine Learning

In machine learning, a hypothesis is a mathematical function or model that converts input data into output predictions. The model's first belief or explanation is based on the facts supplied. The hypothesis is typically expressed as a collection of parameters characterizing the behavior of the model.

If we're building a model to predict the price of a property based on its size and location. The hypothesis function may look something like this −

$$\mathrm{h(x)\:=\:θ0\:+\:θ1\:*\:x1\:+\:θ2\:*\:x2}$$

The hypothesis function is h(x), its input data is x, the model's parameters are 0, 1, and 2, and the features are x1 and x2.

The machine learning model's purpose is to discover the optimal values for parameters 0 through 2 that minimize the difference between projected and actual output labels.

To put it another way, we're looking for the hypothesis function that best represents the underlying link between the input and output data.

Types of Hypotheses in Machine Learning

The next step is to build a hypothesis after identifying the problem and obtaining evidence. A hypothesis is an explanation or solution to a problem based on insufficient data. It acts as a springboard for further investigation and experimentation. A hypothesis is a machine learning function that converts inputs to outputs based on some assumptions. A good hypothesis contributes to the creation of an accurate and efficient machine-learning model. Several machine learning theories are as follows −

1. Null Hypothesis

A null hypothesis is a basic hypothesis that states that no link exists between the independent and dependent variables. In other words, it assumes the independent variable has no influence on the dependent variable. It is symbolized by the symbol H0. If the p-value falls outside the significance level, the null hypothesis is typically rejected (). If the null hypothesis is correct, the coefficient of determination is the probability of rejecting it. A null hypothesis is involved in test findings such as t-tests and ANOVA.

2. Alternative Hypothesis

An alternative hypothesis is a hypothesis that contradicts the null hypothesis. It assumes that there is a relationship between the independent and dependent variables. In other words, it assumes that there is an effect of the independent variable on the dependent variable. It is denoted by Ha. An alternative hypothesis is generally accepted if the p-value is less than the significance level (α). An alternative hypothesis is also known as a research hypothesis.

3. One-tailed Hypothesis

A one-tailed test is a type of significance test in which the region of rejection is located at one end of the sample distribution. It denotes that the estimated test parameter is more or less than the crucial value, implying that the alternative hypothesis rather than the null hypothesis should be accepted. It is most commonly used in the chi-square distribution, where all of the crucial areas, related to, are put in either of the two tails. Left-tailed or right-tailed one-tailed tests are both possible.

4. Two-tailed Hypothesis

The two-tailed test is a hypothesis test in which the region of rejection or critical area is on both ends of the normal distribution. It determines whether the sample tested falls within or outside a certain range of values, and an alternative hypothesis is accepted if the calculated value falls in either of the two tails of the probability distribution. α is bifurcated into two equal parts, and the estimated parameter is either above or below the assumed parameter, so extreme values work as evidence against the null hypothesis.

Overall, the hypothesis plays a critical role in the machine learning model. It provides a starting point for the model to make predictions and helps to guide the learning process. The accuracy of the hypothesis is evaluated using various metrics like mean squared error or accuracy.

The hypothesis is a mathematical function or model that converts input data into output predictions, typically expressed as a collection of parameters characterizing the behavior of the model. It is an explanation or solution to a problem based on insufficient data. A good hypothesis contributes to the creation of an accurate and efficient machine-learning model. A two-tailed hypothesis is used when there is no prior knowledge or theoretical basis to infer a certain direction of the link.

• Related Articles
• What is Machine Learning?
• What is momentum in Machine Learning?
• What is Epoch in Machine Learning?
• What is Standardization in Machine Learning
• What is Q-learning with respect to reinforcement learning in Machine Learning?
• What is Bayes Theorem in Machine Learning
• What is field Mapping in Machine Learning?
• What is Parameter Extraction in Machine Learning
• What is Tpot AutoML in machine learning?
• What is Projection Perspective in Machine Learning?
• What is Grouped Convolution in Machine Learning?
• What is a Neural Network in Machine Learning?
• What is corporate fraud detection in machine learning?
• What is Linear Algebra Application in Machine Learning
• What is Continuous Kernel Convolution in machine learning?

Kickstart Your Career

Get certified by completing the course

Introduction of Hypothesis in Statistics and Machine Learning

Shivam Mishra

Analytics Vidhya

What is Hypothesis in Statistics and Machine learning?

The topic of Hypothesis in Machine Learning can be confusing for beginner because it is related to Statistics(Statistical Hypothesis).

Here,we will study the difference between a hypothesis in science, in statistics, and in machine learning.

Table of content:-

• What is Hypothesis?
• Hypothesis in Statistics.
• Hypothesis in Machine Learning.

1.What is Hypothesis?

A hypothesis (plural hypotheses ) is a proposed explanation for a phenomenon .

The hypothesis must be framed before the outcome of the test is known.

A good Hypothesis fits the evidence and can be used to make predictions about new observations.

The Hypothesis that best fits the evidence and can be used to make predictions is called a theory.

Scientific Hypothesis:-

People refer to a trial solution to a problem as a hypothesis, often called an “ educated guess ” because it provides a suggested outcome based on the evidence. However, some scientists reject the term “educated guess” as incorrect. Experimenters may test and reject several hypotheses before solving the problem. ~ wikipedia

2.Hypothesis in Statistics.

A Hypothesis is an assertion or conjecture about the parameter(s) of population distribution(s).

Much of statistics is concerned with the relationship between observations.

Statistical hypothesis tests are techniques used to calculate a critical value and it can be interpreted in order to determine how likely it is to observe the effect if a relationship does not exist.

If the likelihood is very small, then it suggests that the effect is probably real. If the likelihood is large, then we may have observed a statistical fluctuation, and the effect is probably not real.

Types of Hypothesis

Null Hypothesis(H0) :- A Hypothesis which is to be actually tested for acceptence or rejection is termed as Null hypothesis. Alternative Hypothesis(H1) :- It is a statement about the population parameter, which gives an alternarive to the Null Hypothesis(H0), within the range of pertinent values of the parameter, i.e., if H0 is accepted, what hypothesis is to be rejected and vice versa.

In short, it is a probabilistic explanation about the presence of a relationship between observations.

3. Hypothesis in Machine Learning

A model that approximates the target function and performs mappings of inputs to outputs is called a hypothesis in machine learning.

The choice of algorithm (e.g. neural network) and the configuration of the algorithm (e.g. network topology and hyperparameters) define the space of possible hypothesis that the model may represent.

The framing of machine learning is common and help us to understand the choice of algorithm, the problem of learning and generalization, and even the bias-variance trade-off. For example, the training dataset is used to learn a hypothesis and the test dataset is used to evaluate it.

• h ( hypothesis ) : A single hypothesis, e.g. an instance or specific candidate model that maps inputs to outputs and can be evaluated and used to make predictions.
• H ( hypothesis set ) : A space of possible hypotheses for mapping inputs to outputs that can be searched, often constrained by the choice of the framing of the problem, the choice of model and the choice of model configuration.

In short, model that approximates a target function for mapping examples of inputs to outputs.

Contact me through:-

LinkedIn:- https://www.linkedin.com/in/shivam-mishra-a03815185/

Email:- [email protected]

Twitter:- https://twitter.com/ishivammishra17

Written by Shivam Mishra

I am a student of masters. I like to support our data science community.

Text to speech

• Prompt Library
• DS/AI Trends
• Stats Tools
• Interview Questions
• Generative AI
• Machine Learning
• Deep Learning

Hypothesis Testing Steps & Examples

Table of Contents

What is a Hypothesis testing?

As per the definition from Oxford languages, a hypothesis is a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation. As per the Dictionary page on Hypothesis , Hypothesis means a proposition or set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide investigation (working hypothesis) or accepted as highly probable in the light of established facts.

The hypothesis can be defined as the claim that can either be related to the truth about something that exists in the world, or, truth about something that’s needs to be established a fresh . In simple words, another word for the hypothesis is the “claim” . Until the claim is proven to be true, it is called the hypothesis. Once the claim is proved, it becomes the new truth or new knowledge about the thing. For example , let’s say that a claim is made that students studying for more than 6 hours a day gets more than 90% of marks in their examination. Now, this is just a claim or a hypothesis and not the truth in the real world. However, in order for the claim to become the truth for widespread adoption, it needs to be proved using pieces of evidence, e.g., data.  In order to reject this claim or otherwise, one needs to do some empirical analysis by gathering data samples and evaluating the claim. The process of gathering data and evaluating the claims or hypotheses with the goal to reject or otherwise (failing to reject) can be called as hypothesis testing . Note the wordings – “failing to reject”. It means that we don’t have enough evidence to reject the claim. Thus, until the time that new evidence comes up, the claim can be considered the truth. There are different techniques to test the hypothesis in order to reach the conclusion of whether the hypothesis can be used to represent the truth of the world.

One must note that the hypothesis testing never constitutes a proof that the hypothesis is absolute truth based on the observations. It only provides added support to consider the hypothesis as truth until the time that new evidences can against the hypotheses can be gathered. We can never be 100% sure about truth related to those hypotheses based on the hypothesis testing.

Simply speaking, hypothesis testing is a framework that can be used to assert whether the claim or the hypothesis made about a real-world/real-life event can be seen as the truth or otherwise based on the given data (evidences).

Hypothesis Testing Examples

Before we get ahead and start understanding more details about hypothesis and hypothesis testing steps, lets take a look at some  real-world examples  of how to think about hypothesis and hypothesis testing when dealing with real-world problems :

• Customers are churning because they ain’t getting response to their complaints or issues
• Customers are churning because there are other competitive services in the market which are providing these services at lower cost.
• Customers are churning because there are other competitive services which are providing more services at the same cost.
• It is claimed that a 500 gm sugar packet for a particular brand, say XYZA, contains sugar of less than 500 gm, say around 480gm.  Can this claim be taken as truth? How do we know that this claim is true? This is a hypothesis until proved.
• A group of doctors claims that quitting smoking increases lifespan. Can this claim be taken as new truth? The hypothesis is that quitting smoking results in an increase in lifespan.
• It is claimed that brisk walking for half an hour every day reverses diabetes. In order to accept this in your lifestyle, you may need evidence that supports this claim or hypothesis.
• It is claimed that doing Pranayama yoga for 30 minutes a day can help in easing stress by 50%. This can be termed as hypothesis and would require testing / validation for it to be established as a truth and recommended for widespread adoption.
• One common real-life example of hypothesis testing is election polling. In order to predict the outcome of an election, pollsters take a sample of the population and ask them who they plan to vote for. They then use hypothesis testing to assess whether their sample is representative of the population as a whole. If the results of the hypothesis test are significant, it means that the sample is representative and that the poll can be used to predict the outcome of the election. However, if the results are not significant, it means that the sample is not representative and that the poll should not be used to make predictions.
• Machine learning models make predictions based on the input data. Each of the machine learning model representing a function approximation can be taken as a hypothesis. All different models constitute what is called as hypothesis space .
• As part of a linear regression machine learning model , it is claimed that there is a relationship between the response variables and predictor variables? Can this hypothesis or claim be taken as truth? Let’s say, the hypothesis is that the housing price depends upon the average income of people already staying in the locality. How true is this hypothesis or claim? The relationship between response variable and each of the predictor variables can be evaluated using T-test and T-statistics .
• For linear regression model , one of the hypothesis is that there is no relationship between the response variable and any of the predictor variables. Thus, if b1, b2, b3 are three parameters, all of them is equal to 0. b1 = b2 = b3 = 0. This is where one performs F-test and use F-statistics to test this hypothesis.

You may note different hypotheses which are listed above. The next step would be validate some of these hypotheses. This is where data scientists will come into picture. One or more data scientists may be asked to work on different hypotheses. This would result in these data scientists looking for appropriate data related to the hypothesis they are working. This section will be detailed out in near future.

State the Hypothesis to begin Hypothesis Testing

The first step to hypothesis testing is defining or stating a hypothesis. Before the hypothesis can be tested, we need to formulate the hypothesis in terms of mathematical expressions. There are two important aspects to pay attention to, prior to the formulation of the hypothesis. The following represents different types of hypothesis that could be put to hypothesis testing:

• Claim made against the well-established fact : The case in which a fact is well-established, or accepted as truth or “knowledge” and a new claim is made about this well-established fact. For example , when you buy a packet of 500 gm of sugar, you assume that the packet does contain at the minimum 500 gm of sugar and not any less, based on the label of 500 gm on the packet. In this case, the fact is given or assumed to be the truth. A new claim can be made that the 500 gm sugar contains sugar weighing less than 500 gm. This claim needs to be tested before it is accepted as truth. Such cases could be considered for hypothesis testing if this is claimed that the assumption or the default state of being is not true. The claim to be established as new truth can be stated as “alternate hypothesis”. The opposite state can be stated as “null hypothesis”. Here the claim that the 500 gm packet consists of sugar less than 500 grams would be stated as alternate hypothesis. The opposite state which is the sugar packet consists 500 gm is null hypothesis.
• Claim to establish the new truth : The case in which there is some claim made about the reality that exists in the world (fact). For example , the fact that the housing price depends upon the average income of people already staying in the locality can be considered as a claim and not assumed to be true. Another example could be the claim that running 5 miles a day would result in a reduction of 10 kg of weight within a month. There could be varied such claims which when required to be proved as true have to go through hypothesis testing. The claim to be established as new truth can be stated as “alternate hypothesis”. The opposite state can be stated as “null hypothesis”. Running 5 miles a day would result in reduction of 10 kg within a month would be stated as alternate hypothesis.

Based on the above considerations, the following hypothesis can be stated for doing hypothesis testing.

• The packet of 500 gm of sugar contains sugar of weight less than 500 gm. (Claim made against the established fact). This is a new knowledge which requires hypothesis testing to get established and acted upon.
• The housing price depends upon the average income of the people staying in the locality. This is a new knowledge which requires hypothesis testing to get established and acted upon.
• Running 5 miles a day results in a reduction of 10 kg of weight within a month. This is a new knowledge which requires hypothesis testing to get established for widespread adoption.

Formulate Null & Alternate Hypothesis as Next Step

Once the hypothesis is defined or stated, the next step is to formulate the null and alternate hypothesis in order to begin hypothesis testing as described above.

What is a null hypothesis?

In the case where the given statement is a well-established fact or default state of being in the real world, one can call it a null hypothesis (in the simpler word, nothing new). Well-established facts don’t need any hypothesis testing and hence can be called the null hypothesis. In cases, when there are any new claims made which is not well established in the real world, the null hypothesis can be thought of as the default state or opposite state of that claim. For example , in the previous section, the claim or hypothesis is made that the students studying for more than 6 hours a day gets more than 90% of marks in their examination. The null hypothesis, in this case, will be that the claim is not true or real. The null hypothesis can be stated that there is no relationship or association between the students reading more than 6 hours a day and they getting 90% of the marks. Any occurrence is only a chance occurrence. Another example of hypothesis is when somebody is alleged that they have performed a crime.

Null hypothesis is denoted by letter H with 0, e.g., [latex]H_0[/latex]

What is an alternate hypothesis?

When the given statement is a claim (unexpected event in the real world) and not yet proven, one can call/formulate it as an alternate hypothesis and accordingly define a null hypothesis which is the opposite state of the hypothesis. The alternate hypothesis is a new knowledge or truth that needs to be established. In simple words, the hypothesis or claim that needs to be tested against reality in the real world can be termed the alternate hypothesis. In order to reach a conclusion that the claim (alternate hypothesis) can be considered the new knowledge or truth (based on the available evidence), it would be important to reject the null hypothesis. It should be noted that null and alternate hypotheses are mutually exclusive and at the same time asymmetric. In the example given in the previous section, the claim that the students studying for more than 6 hours get more than 90% of marks can be termed as the alternate hypothesis.

Alternate hypothesis is denoted with H subscript a, e.g., [latex]H_a[/latex]

Once the hypothesis is formulated as null([latex]H_0[/latex]) and alternate hypothesis ([latex]H_a[/latex]), there are two possible outcomes that can happen from hypothesis testing. These outcomes are the following:

• Reject the null hypothesis : There is enough evidence based on which one can reject the null hypothesis. Let’s understand this with the help of an example provided earlier in this section. The null hypothesis is that there is no relationship between the students studying more than 6 hours a day and getting more than 90% marks. In a sample of 30 students studying more than 6 hours a day, it was found that they scored 91% marks. Given that the null hypothesis is true, this kind of hypothesis testing result will be highly unlikely. This kind of result can’t happen by chance. That would mean that the claim can be taken as the new truth or new knowledge in the real world. One can go and take further samples of 30 students to perform some more testing to validate the hypothesis. If similar results show up with other tests, it can be said with very high confidence that there is enough evidence to reject the null hypothesis that there is no relationship between the students studying more than 6 hours a day and getting more than 90% marks. In such cases, one can go to accept the claim as new truth that the students studying more than 6 hours a day get more than 90% marks. The hypothesis can be considered the new truth until the time that new tests provide evidence against this claim.
• Fail to reject the null hypothesis : There is not enough evidence-based on which one can reject the null hypothesis (well-established fact or reality). Thus, one would fail to reject the null hypothesis. In a sample of 30 students studying more than 6 hours a day, the students were found to score 75%. Given that the null hypothesis is true, this kind of result is fairly likely or expected. With the given sample, one can’t reject the null hypothesis that there is no relationship between the students studying more than 6 hours a day and getting more than 90% marks.

Examples of formulating the null and alternate hypothesis

The following are some examples of the null and alternate hypothesis.

Hypothesis Testing Steps

Here is the diagram which represents the workflow of Hypothesis Testing.

Figure 1. Hypothesis Testing Steps

Based on the above, the following are some of the  steps to be taken when doing hypothesis testing:

• State the hypothesis : First and foremost, the hypothesis needs to be stated. The hypothesis could either be the statement that is assumed to be true or the claim which is made to be true.
• Formulate the hypothesis : This step requires one to identify the Null and Alternate hypotheses or in simple words, formulate the hypothesis. Take an example of the canned sauce weighing 500 gm as the Null Hypothesis.
• Set the criteria for a decision : Identify test statistics that could be used to assess the Null Hypothesis. The test statistics with the above example would be the average weight of the sugar packet, and t-statistics would be used to determine the P-value. For different kinds of problems, different kinds of statistics including Z-statistics, T-statistics, F-statistics, etc can be used.
• Identify the level of significance (alpha) : Before starting the hypothesis testing, one would be required to set the significance level (also called as  alpha ) which represents the value for which a P-value less than or equal to  alpha  is considered statistically significant. Typical values of  alpha  are 0.1, 0.05, and 0.01. In case the P-value is evaluated as statistically significant, the null hypothesis is rejected. In case, the P-value is more than the  alpha  value, the null hypothesis is failed to be rejected.
• Compute the test statistics : Next step is to calculate the test statistics (z-test, t-test, f-test, etc) to determine the P-value. If the sample size is more than 30, it is recommended to use z-statistics. Otherwise, t-statistics could be used. In the current example where 20 packets of canned sauce is selected for hypothesis testing, t-statistics will be calculated for the mean value of 505 gm (sample mean). The t-statistics would then be calculated as the difference of 505 gm (sample mean) and the population means (500 gm) divided by the sample standard deviation divided by the square root of sample size (20).
• Calculate the P-value of the test statistics : Once the test statistics have been calculated, find the P-value using either of t-table or a z-table. P-value is the probability of obtaining a test statistic (t-score or z-score) equal to or more extreme than the result obtained from the sample data, given that the null hypothesis H0 is true.
• Compare P-value with the level of significance : The significance level is set as the allowable range within which if the value appears, one will be failed to reject the Null Hypothesis. This region is also called as Non-rejection region . The value of alpha is compared with the p-value. If the p-value is less than the significance level, the test is statistically significant and hence, the null hypothesis will be rejected.

P-Value: Key to Statistical Hypothesis Testing

Once you formulate the hypotheses, there is the need to test those hypotheses. Meaning, say that the null hypothesis is stated as the statement that housing price does not depend upon the average income of people staying in the locality, it would be required to be tested by taking samples of housing prices and, based on the test results, this Null hypothesis could either be rejected or failed to be rejected . In hypothesis testing, the following two are the outcomes:

• Reject the Null hypothesis
• Fail to Reject the Null hypothesis

Take the above example of the sugar packet weighing 500 gm. The Null hypothesis is set as the statement that the sugar packet weighs 500 gm. After taking a sample of 20 sugar packets and testing/taking its weight, it was found that the average weight of the sugar packets came to 495 gm. The test statistics (t-statistics) were calculated for this sample and the P-value was determined. Let’s say the P-value was found to be 15%. Assuming that the level of significance is selected to be 5%, the test statistic is not statistically significant (P-value > 5%) and thus, the null hypothesis fails to get rejected. Thus, one could safely conclude that the sugar packet does weigh 500 gm. However, if the average weight of canned sauce would have found to be 465 gm, this is way beyond/away from the mean value of 500 gm and one could have ended up rejecting the Null Hypothesis based on the P-value .

Hypothesis Testing for Problem Analysis & Solution Implementation

Hypothesis testing can be applied in both problem analysis and solution implementation. The following represents method on how you can apply hypothesis testing technique for both problem and solution space:

• Problem Analysis : Hypothesis testing is a systematic way to validate assumptions or educated guesses during problem analysis. It allows for a structured investigation into the nature of a problem and its potential root causes. In this process, a null hypothesis and an alternative hypothesis are usually defined. The null hypothesis generally asserts that no significant change or effect exists, while the alternative hypothesis posits the opposite. Through controlled experiments, data collection, or statistical analysis, these hypotheses are then tested to determine their validity. For example, if a software company notices a sudden increase in user churn rate, they might hypothesize that the recent update to their application is the root cause. The null hypothesis could be that the update has no effect on churn rate, while the alternative hypothesis would assert that the update significantly impacts the churn rate. By analyzing user behavior and feedback before and after the update, and perhaps running A/B tests where one user group has the update and another doesn’t, the company can test these hypotheses. If the alternative hypothesis is confirmed, the company can then focus on identifying specific issues in the update that may be causing the increased churn, thereby moving closer to a solution.
• Solution Implementation : Hypothesis testing can also be a valuable tool during the solution implementation phase, serving as a method to evaluate the effectiveness of proposed remedies. By setting up a specific hypothesis about the expected outcome of a solution, organizations can create targeted metrics and KPIs to measure success. For example, if a retail business is facing low customer retention rates, they might implement a loyalty program as a solution. The hypothesis could be that introducing a loyalty program will increase customer retention by at least 15% within six months. The null hypothesis would state that the loyalty program has no significant effect on retention rates. To test this, the company can compare retention metrics from before and after the program’s implementation, possibly even setting up control groups for more robust analysis. By applying statistical tests to this data, the company can determine whether their hypothesis is confirmed or refuted, thereby gauging the effectiveness of their solution and making data-driven decisions for future actions.
• Tests of Significance
• Hypothesis testing for the Mean
• z-statistics vs t-statistics (Khan Academy)

Hypothesis testing quiz

The claim that needs to be established is set as ____________, the outcome of hypothesis testing is _________.

Please select 2 correct answers

P-value is defined as the probability of obtaining the result as extreme given the null hypothesis is true

There is a claim that doing pranayama yoga results in reversing diabetes. which of the following is true about null hypothesis.

In this post, you learned about hypothesis testing and related nuances such as the null and alternate hypothesis formulation techniques, ways to go about doing hypothesis testing etc. In data science, one of the reasons why one needs to understand the concepts of hypothesis testing is the need to verify the relationship between the dependent (response) and independent (predictor) variables. One would, thus, need to understand the related concepts such as hypothesis formulation into null and alternate hypothesis, level of significance, test statistics calculation, P-value, etc. Given that the relationship between dependent and independent variables is a sort of hypothesis or claim , the null hypothesis could be set as the scenario where there is no relationship between dependent and independent variables.

Recent Posts

• ROC Curve & AUC Explained with Python Examples - September 8, 2024
• Accuracy, Precision, Recall & F1-Score – Python Examples - August 28, 2024
• Logistic Regression in Machine Learning: Python Example - August 26, 2024

Ajitesh Kumar

Leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

• Search for:

ChatGPT Prompts (250+)

• Generate Design Ideas for App
• Expand Feature Set of App
• Create a User Journey Map for App
• Generate Visual Design Ideas for App
• Generate a List of Competitors for App
• ROC Curve & AUC Explained with Python Examples
• Accuracy, Precision, Recall & F1-Score – Python Examples
• Logistic Regression in Machine Learning: Python Example
• Reducing Overfitting vs Models Complexity: Machine Learning
• Model Parallelism vs Data Parallelism: Examples

Data Science / AI Trends

• • Prepend any arxiv.org link with talk2 to load the paper into a responsive chat application
• • Custom LLM and AI Agents (RAG) On Structured + Unstructured Data - AI Brain For Your Organization
• • Guides, papers, lecture, notebooks and resources for prompt engineering
• • Common tricks to make LLMs efficient and stable
• • Machine learning in finance

Free Online Tools

• Create Scatter Plots Online for your Excel Data
• Histogram / Frequency Distribution Creation Tool
• Online Pie Chart Maker Tool
• Z-test vs T-test Decision Tool
• Independent samples t-test calculator

Recent Comments

I found it very helpful. However the differences are not too understandable for me

Very Nice Explaination. Thankyiu very much,

in your case E respresent Member or Oraganization which include on e or more peers?

Such a informative post. Keep it up

Thank you....for your support. you given a good solution for me.

• School Guide
• Mathematics
• Number System and Arithmetic
• Trigonometry
• Probability
• Mensuration
• Maths Formulas
• Class 8 Maths Notes
• Class 9 Maths Notes
• Class 10 Maths Notes
• Class 11 Maths Notes
• Class 12 Maths Notes

Hypothesis | Definition, Meaning and Examples

Hypothesis is a hypothesis is fundamental concept in the world of research and statistics. It is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables.

Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion . Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what hypothesis is, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

Table of Content

What is Hypothesis?

Characteristics of hypothesis, sources of hypothesis, types of hypothesis, functions of hypothesis, how hypothesis help in scientific research.

Hypothesis is a suggested idea or an educated guess or a proposed explanation made based on limited evidence, serving as a starting point for further study. They are meant to lead to more investigation.

It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes. Example: Studying more can help you do better on tests. Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together. Example: How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live. A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing. Example: Drinking more sweet drinks is linked to a higher body weight score. Too much stress makes people less productive at work.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes. Example: Drinking caffeine can affect how well you sleep. People often like different kinds of music based on their gender.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information. Example: The average test scores of Group A and Group B are not much different. There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one. Example: Patients on Diet A have much different cholesterol levels than those following Diet B. Exposure to a certain type of light can change how plants grow compared to normal sunlight.

Statistical Hypothesis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only. Example: The average smarts score of kids in a certain school area is 100. The usual time it takes to finish a job using Method A is the same as with Method B.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely. Example: Having more kids go to early learning classes helps them do better in school when they get older. Using specific ways of talking affects how much customers get involved in marketing activities.

Associative Hypothesis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing. Example: Regular exercise helps to lower the chances of heart disease. Going to school more can help people make more money.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change. Example: Playing violent video games makes teens more likely to act aggressively. Less clean air directly impacts breathing health in city populations.

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

People Also View:

Mathematics Maths Formulas Branches of Mathematics

Hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge . It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.

The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .

The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.

Hypothesis – FAQs

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

Please Login to comment...

Similar reads.

• Geeks Premier League
• School Learning
• Geeks Premier League 2023
• Maths-Class-12
• Best Twitch Extensions for 2024: Top Tools for Viewers and Streamers
• Discord Emojis List 2024: Copy and Paste
• Best Adblockers for Twitch TV: Enjoy Ad-Free Streaming in 2024
• PS4 vs. PS5: Which PlayStation Should You Buy in 2024?
• 15 Most Important Aptitude Topics For Placements [2024]

Improve your Coding Skills with Practice

What kind of Experience do you want to share?