{"id":2552,"date":"2023-03-01T10:40:04","date_gmt":"2023-03-01T10:40:04","guid":{"rendered":"https:\/\/pickl.ai\/blog\/?p=2552"},"modified":"2024-08-22T07:25:39","modified_gmt":"2024-08-22T07:25:39","slug":"hypothesis-testing-in-statistics","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/","title":{"rendered":"What is Hypothesis Testing in Statistics? Types and Steps"},"content":{"rendered":"<p><b>Summary: <\/b><span style=\"font-weight: 400;\">Hypothesis testing in statistics uses sample data to assess population assumptions, involving types and steps to guide informed decisions.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#What_is_Hypothesis_testing\" >What is Hypothesis testing?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Types_of_Hypothesis_Testing\" >Types of Hypothesis Testing<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Null_Hypothesis_H0_and_Alternative_Hypothesis_Ha\" >Null Hypothesis (H0) and Alternative Hypothesis (Ha)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Simple_and_Composite_Hypothesis_Testing\" >Simple and Composite Hypothesis Testing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#One-Tailed_and_Two-Tailed_Hypothesis_Testing\" >One-Tailed and Two-Tailed Hypothesis Testing<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Steps_in_Hypothesis_Testing\" >Steps in Hypothesis Testing<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Step_1_Stating_the_hypotheses\" >Step 1: Stating the hypotheses<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Step_2_Setting_the_significance_level\" >Step 2: Setting the significance level<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Step_3_Collecting_the_data\" >Step 3: Collecting the data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Step_4_Calculating_the_test_statistic\" >Step 4: Calculating the test statistic<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Step_5_Calculating_the_p-value\" >Step 5: Calculating the p-value<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Step_6_Making_a_decision_and_interpreting_the_results\" >Step 6: Making a decision and interpreting the results<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Hypothesis_Testing_in_Statistics_Examples\" >Hypothesis Testing in Statistics Examples<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#For_performing_a_hypothesis_test_on_the_above_problem_the_steps_need_to_be_followed_are\" >For performing a hypothesis test on the above problem, the steps need to be followed are:<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Common_Errors_in_Hypothesis_Testing\" >Common Errors in Hypothesis Testing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#What_is_Hypothesis_Testing_in_Statistics\" >What is Hypothesis Testing in Statistics?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#What_are_the_types_of_Hypothesis_Testing\" >What are the types of Hypothesis Testing?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#What_are_the_steps_in_Hypothesis_Testing\" >What are the steps in Hypothesis Testing?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/#Closing_Words\" >Closing Words<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><b>Introduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Hypothesis testing evaluates and tests a proposed hypothesis or claim about a population parameter against the evidence inferred from the sample data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This article delves into the fundamental statistical hypothesis testing concept, covering its types and steps, defining the significance level and interpreting p-values. It also addresses common errors encountered in hypothesis testing, offering insights crucial for accurate statistical inference.<\/span><\/p>\n<p><b>Must Check:<br \/>\n<\/b><a href=\"https:\/\/pickl.ai\/blog\/a-comprehensive-guide-to-descriptive-statistics\/\"><span style=\"font-weight: 400;\">A Comprehensive Guide to Descriptive Statistics<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/crucial-statistics-interview-questions-for-data-science-success\/\"><span style=\"font-weight: 400;\">Crucial Statistics Interview Questions for Data Science Success<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"what-is-hypothesis-testing\"><span class=\"ez-toc-section\" id=\"What_is_Hypothesis_testing\"><\/span><b>What is Hypothesis testing?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Hypothesis testing, meaning the process of statistically evaluating assumptions about a population based on sample data, plays a pivotal role in scientific and statistical research.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It involves making inferences about a population by assessing the probability of observed data assuming a true null hypothesis. This method is widely applied across various domains, such as business, healthcare, and engineering, to drive informed decision-making.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In practice, hypothesis testing begins with formulating a null hypothesis, typically stating no effect or no difference between groups, and an alternative hypothesis suggesting the presence of an impact or difference.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Through rigorous statistical analysis, researchers gather evidence from sample data to either reject the null hypothesis in favour of the alternative or fail to reject it due to insufficient evidence. This process helps validate theories, understand relationships, and guide decisions based on empirical evidence rather than assumptions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hypothesis testing guides marketing strategies or product innovations in business, validates treatment efficacy in healthcare, and ensures product reliability in engineering. By providing a structured approach to concluding data, hypothesis testing is a cornerstone of evidence-based decision-making across diverse fields.<\/span><\/p>\n<p><b>See More:\u00a0<\/b><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/inferential-statistics-to-boost-your-career-in-data-science\/\"><span style=\"font-weight: 400;\">Inferential Statistics to Boost Your Career in Data Science<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/types-of-variables-in-statistics\/\"><span style=\"font-weight: 400;\">Types of Variables in Statistics with Examples<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"types-of-hypothesis-testing\"><span class=\"ez-toc-section\" id=\"Types_of_Hypothesis_Testing\"><\/span><b>Types of Hypothesis Testing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Understanding types of hypothesis testing is crucial for data analysis. It enables researchers to choose the right test to validate their hypotheses accurately. Significant types of hypothesis testing are:<\/span><\/p>\n<h3 id=\"null-hypothesis-h0-and-alternative-hypothesis-ha\"><span class=\"ez-toc-section\" id=\"Null_Hypothesis_H0_and_Alternative_Hypothesis_Ha\"><\/span><b>Null Hypothesis (H0) and Alternative Hypothesis (Ha)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Null_hypothesis\"><span style=\"font-weight: 400;\">null hypothesis<\/span><\/a><span style=\"font-weight: 400;\"> states that there is no significant association between variables. It is to be tested; any observed difference is merely due to chance.<\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Alternative_hypothesis\"><span style=\"font-weight: 400;\"> The alternative hypothesis states<\/span><\/a><span style=\"font-weight: 400;\"> that there is a difference or association between variables. If the null hypothesis is rejected and the observed difference is not due to chance, it is the accepting hypothesis.<\/span><\/p>\n<h3 id=\"simple-and-composite-hypothesis-testing\"><span class=\"ez-toc-section\" id=\"Simple_and_Composite_Hypothesis_Testing\"><\/span><b>Simple and Composite Hypothesis Testing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In hypothesis testing, null and alternative hypotheses can be simple and composite hypotheses. A simple hypothesis specifies a particular value for a population parameter. For example, the null hypothesis that the mean height of a population is 173 cm is a simple hypothesis. A <\/span><a href=\"https:\/\/www.analytics-toolkit.com\/glossary\/composite-hypothesis\/#:~:text=In%20hypothesis%20testing%20a%20composite,%2C%202%20or%203%2C1245.\"><span style=\"font-weight: 400;\">composite hypothesis<\/span><\/a><span style=\"font-weight: 400;\"> specifies a range of values for a population parameter. For example, the alternative hypothesis that the mean height of a population is not 173 cm is a composite hypothesis.<\/span><\/p>\n<h3 id=\"one-tailed-and-two-tailed-hypothesis-testing\"><span class=\"ez-toc-section\" id=\"One-Tailed_and_Two-Tailed_Hypothesis_Testing\"><\/span><b>One-Tailed and Two-Tailed Hypothesis Testing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In hypothesis testing, alternative hypotheses can be one-tailed and two-tailed. The <\/span><a href=\"https:\/\/www.investopedia.com\/terms\/o\/one-tailed-test.asp\"><span style=\"font-weight: 400;\">one-tailed<\/span><\/a><span style=\"font-weight: 400;\"> alternative hypothesis specifies the direction of difference between variables. Here, the distribution of the test sample is one-sided, meaning it is either greater or lesser than a specific value.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The two-tailed alternative hypothesis does not specify the direction of difference between variables. Here, the distribution of the test sample is two-sided, meaning it is checked to be greater or less than a range of values.<\/span><\/p>\n<p><b>Read Further:<\/b> <a href=\"https:\/\/pickl.ai\/blog\/difference-between-descriptive-and-inferential-statistics-with-examples\/\"><span style=\"font-weight: 400;\">Difference Between Descriptive and Inferential statistics with Examples<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"steps-in-hypothesis-testing\"><span class=\"ez-toc-section\" id=\"Steps_in_Hypothesis_Testing\"><\/span><b>Steps in Hypothesis Testing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Now, you will understand the steps in hypothesis testing and how it is crucial for data-driven decision-making. It allows one to systematically evaluate assumptions, analyse data, and draw reliable conclusions. Mastery of this process ensures rigorous testing of theories, minimising errors and biases and ultimately enhancing the credibility and validity of research findings.<\/span><\/p>\n<h3 id=\"step-1-stating-the-hypotheses\"><span class=\"ez-toc-section\" id=\"Step_1_Stating_the_hypotheses\"><\/span><b>Step 1: Stating the hypotheses<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Clearly state the null and alternative hypotheses based on your research area. The null hypothesis represents the default assumption, while the alternative hypothesis reflects the desired outcome you aim to prove through your analysis and experiments. This step sets the foundation for your <\/span><a href=\"https:\/\/pickl.ai\/blog\/what-is-data-science-comprehensive-guide\/\"><span style=\"font-weight: 400;\">Data Science<\/span><\/a><span style=\"font-weight: 400;\"> project.<\/span><\/p>\n<p><b>Also Check:<\/b><a href=\"https:\/\/pickl.ai\/blog\/top-data-science-projects-on-github\/\"> <span style=\"font-weight: 400;\">What are the Best Data Science Projects on GitHub?<\/span><\/a><\/p>\n<h3 id=\"step-2-setting-the-significance-level\"><span class=\"ez-toc-section\" id=\"Step_2_Setting_the_significance_level\"><\/span><b>Step 2: Setting the significance level<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The significance level is denoted by alpha (\u03b1). It is the probability of rejecting the null hypothesis when it is true. The significance level is usually 0.05 or 0.01, meaning the chance of 5% or 1% in accepting a Type 1 error (rejection of true null hypothesis).\u00a0<\/span><\/p>\n<h3 id=\"step-3-collecting-the-data\"><span class=\"ez-toc-section\" id=\"Step_3_Collecting_the_data\"><\/span><b>Step 3: Collecting the data<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Collect data by conducting a study or experiment to test the hypothesis. Focus on gathering random data to ensure unbiased results. Random data reduces the risk of systematic errors and provides a more accurate representation of the population or phenomenon being studied.<\/span><\/p>\n<h3 id=\"step-4-calculating-the-test-statistic\"><span class=\"ez-toc-section\" id=\"Step_4_Calculating_the_test_statistic\"><\/span><b>Step 4: Calculating the test statistic<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The test statistic measures the deviation of sample data from the null hypothesis. Frequently used test statistics are the chi-square test, t-test, z-test, and F-test. The type of test statistic used depends on the hypothesis being tested and the level of data measurement.<\/span><\/p>\n<h3 id=\"step-5-calculating-the-p-value\"><span class=\"ez-toc-section\" id=\"Step_5_Calculating_the_p-value\"><\/span><b>Step 5: Calculating the p-value<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The p-value is the probability of observing the test statistic assuming the null hypothesis is true or the likelihood of the null hypothesis being rejected.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A small p-value (less than the significance level) indicates rejection of the null hypothesis, and a large p-value (more significant than the significance level) suggests that the null hypothesis cannot be rejected.<\/span><\/p>\n<h3 id=\"step-6-making-a-decision-and-interpreting-the-results\"><span class=\"ez-toc-section\" id=\"Step_6_Making_a_decision_and_interpreting_the_results\"><\/span><b>Step 6: Making a decision and interpreting the results<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Finally, a decision is made based on the p-value and the significance level. Suppose the p-value is less than the significance level. In that case, the null hypothesis is rejected in favour of the alternative hypothesis, and the results are said to be statistically significant.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If the p-value exceeds the significance level, the null hypothesis is not rejected, and the results are not statistically significant.<\/span><\/p>\n<p><b>Read Blog: <\/b><a href=\"https:\/\/pickl.ai\/blog\/10-best-statistics-books-for-data-science\/\"><span style=\"font-weight: 400;\">Best Statistics Books for Data Science<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"hypothesis-testing-in-statistics-examples\"><span class=\"ez-toc-section\" id=\"Hypothesis_Testing_in_Statistics_Examples\"><\/span><b>Hypothesis Testing in Statistics Examples<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h2 id=\"\"><b><img fetchpriority=\"high\" decoding=\"async\" class=\"radius-5 aligncenter wp-image-10705 size-full\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2.jpg\" alt=\"Hypothesis Testing in Statistics\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/03\/image2-2-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/b><\/h2>\n<p><b>Example:<\/b><span style=\"font-weight: 400;\"> Suppose a factory produces light bulbs, and the manufacturer claims that the average lifespan of a bulb is 1000 hours. However, you suspect that the bulbs may not last that long. To test your hypothesis, you randomly select a sample of 50 bulbs and test their lifespans. You find that the average lifespan of the sample is 980 hours with a standard deviation of 50 hours.<\/span><\/p>\n<h3 id=\"for-performing-a-hypothesis-test-on-the-above-problem-the-steps-need-to-be-followed-are\"><span class=\"ez-toc-section\" id=\"For_performing_a_hypothesis_test_on_the_above_problem_the_steps_need_to_be_followed_are\"><\/span><b>For performing a hypothesis test on the above problem, the steps need to be followed are:<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The null and alternative hypotheses are defined, with the null hypothesis (H0) that the average lifespan of bulbs is 1000 hours and the alternative hypothesis (Ha) that it is less than 1000 hours.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Choose a significance level of 0.05, i.e., acceptance of a 5% risk of rejecting the null hypothesis when it is true.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The test statistic is calculated using a t-test as the population standard division is unknown. The formula for the t-test is:<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">t = (x\u0304 \u2013 ? ) \/ (s \/\u221an\u00a0 )<\/span><\/p>\n<p><span style=\"font-weight: 400;\">where x\u0304 is the sample mean? is the population mean (to be tested), s is the sample standard deviation, and n is the sample size.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Putting in the values,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">t = (980 \u2013 1000) \/ (50\/\u221a50 ) = -2.24<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">To calculate the p-value, we use the one-tailed hypothesis (as we only want to find whether the lifespan is less than 1000 hours), so we have to find the area under the t-distribution to the left of our test statistic. Using a t-table or a calculator, we found the p-value to be 0.014.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">When we compare the p-value (0.014) to the significance level (0.05), we conclude that the p-value is less than the significance level, which means the average lifespan of bulbs is less than 1000 hours.<\/span><\/li>\n<\/ol>\n<p><b>Note: <\/b><span style=\"font-weight: 400;\">This is just a simple illustrative example. Hypothesis testing in statistics can be more complex, involving more variables, sample sizes, and assumptions.<\/span><\/p>\n<h2 id=\"common-errors-in-hypothesis-testing\"><span class=\"ez-toc-section\" id=\"Common_Errors_in_Hypothesis_Testing\"><\/span><b>Common Errors in Hypothesis Testing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">In hypothesis testing, two common errors can occur: Type 1 and Type 2 errors. A Type 1 error happens when you reject the null hypothesis, even though it is true. Essentially, you believe there is an effect or difference when there isn&#8217;t one.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The likelihood of committing a Type 1 error is represented by alpha (\u03b1), also known as the significance level of the test. Researchers usually set \u03b1 at 0.05, meaning there&#8217;s a 5% risk of making this error.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">On the other hand, a Type 2 error occurs when you fail to reject the null hypothesis when it is false. It means you need to detect an effect or difference that truly exists.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The probability of making a Type 2 error is denoted by beta (\u03b2). While the researcher typically predetermines \u03b1, \u03b2 depends on factors like sample size, effect size, and the chosen \u03b1 level. A high \u03b2, indicating a high chance of Type 2 error, can undermine the test&#8217;s ability to detect actual effects.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Understanding and managing these errors is crucial in hypothesis testing. Researchers aim to minimise \u03b1 to avoid false positives while considering power (1-\u03b2) to ensure they don&#8217;t overlook accurate findings. Balancing these risks helps achieve reliable and valid results in scientific studies.<\/span><\/p>\n<p><b>Read Blog:<\/b> <a href=\"https:\/\/pickl.ai\/blog\/probability-distribution-in-data-science\/\"><span style=\"font-weight: 400;\">Probability Distribution in Data Science<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"what-is-hypothesis-testing-in-statistics\"><span class=\"ez-toc-section\" id=\"What_is_Hypothesis_Testing_in_Statistics\"><\/span><b>What is Hypothesis Testing in Statistics?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Hypothesis testing in statistics evaluates assumptions about a population based on sample data. It involves formulating a null hypothesis (no effect) and an alternative hypothesis (presence of an effect). Through statistical analysis, researchers determine whether to reject the null hypothesis based on the evidence.<\/span><\/p>\n<h3 id=\"what-are-the-types-of-hypothesis-testing\"><span class=\"ez-toc-section\" id=\"What_are_the_types_of_Hypothesis_Testing\"><\/span><b>What are the types of Hypothesis Testing?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Hypothesis testing includes null and alternative hypotheses, simple and composite hypotheses, and one-tailed and two-tailed hypotheses. Null and alternative hypotheses address the presence or absence of effects, while simple and composite hypotheses specify single values or ranges. One-tailed tests focus on one direction, and two-tailed tests examine both directions.<\/span><\/p>\n<h3 id=\"what-are-the-steps-in-hypothesis-testing\"><span class=\"ez-toc-section\" id=\"What_are_the_steps_in_Hypothesis_Testing\"><\/span><b>What are the steps in Hypothesis Testing?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The steps in hypothesis testing include clearly stating the null and alternative hypotheses, setting the significance level (alpha), collecting and analysing sample data, calculating the test statistic, determining the p-value, and making a decision. This process helps validate theories and ensures evidence-based conclusions.<\/span><\/p>\n<h2 id=\"closing-words\"><span class=\"ez-toc-section\" id=\"Closing_Words\"><\/span><b>Closing Words<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Hypothesis testing is crucial for making informed statistics decisions by evaluating assumptions about populations through sample data analysis. This structured method involves formulating null and alternative hypotheses and setting a significance level.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It also includes systematically analysing data to draw reliable conclusions. Understanding and applying these steps ensure rigorous and credible statistical inferences, reducing errors and enhancing decision-making across various fields.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Understand hypothesis testing in statistics: types, steps, and examples for data-driven decisions.\n","protected":false},"author":13,"featured_media":10707,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[46,2346],"tags":[703,705,704,702,701,700],"ppma_author":[2178,2185],"class_list":{"0":"post-2552","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-statistics","9":"tag-hypothesis-testing-in-statistics","10":"tag-hypothesis-testing-in-statistics-examples","11":"tag-hypothesis-testing-meaning","12":"tag-steps-in-hypothesis-testing","13":"tag-types-of-hypothesis-testing","14":"tag-what-is-hypothesis-testing-in-statistics"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Hypothesis Testing in Statistics<\/title>\n<meta name=\"description\" content=\"To make data-driven decisions, learn the essentials of hypothesis testing in statistics, including its types, steps, and examples.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.pickl.ai\/blog\/hypothesis-testing-in-statistics\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is Hypothesis Testing in Statistics? 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