{"id":3426,"date":"2026-08-24T09:20:44","date_gmt":"2026-08-24T09:20:44","guid":{"rendered":"https:\/\/us.allassignmentsupport.com\/blog\/?p=3426"},"modified":"2026-08-24T10:40:04","modified_gmt":"2026-08-24T10:40:04","slug":"how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples","status":"publish","type":"post","link":"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/","title":{"rendered":"How to Perform Hypothesis Testing in Python: A Step-by-Step Guide with Worked Examples"},"content":{"rendered":"<p dir=\"ltr\">Every statistics student eventually reaches the same gap: you can explain what a <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/null-vs-alternative-hypothesis-how-to-write-and-test-them\/\">null and alternative hypothesis<\/a><\/strong> are, you understand what a <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/p-value-explained-simply\/\">p-value<\/a><\/strong> means, but the moment an assignment says &#8220;test this in Python,&#8221; the theory and the code feel like two completely separate courses. This guide closes that gap directly \u2014 it walks through the four hypothesis tests students are assigned most often, using Python&#8217;s <code>scipy.stats<\/code> library, with full working code and a plain-language interpretation of every result.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_69_1 counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title \" >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:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#Setting_Up_What_You_Need_Before_Testing_Anything\" title=\"Setting Up: What You Need Before Testing Anything\">Setting Up: What You Need Before Testing Anything<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#The_General_Workflow_for_Any_Hypothesis_Test_in_Python\" title=\"The General Workflow for Any Hypothesis Test in Python\">The General Workflow for Any Hypothesis Test in Python<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#One-Sample_t-Test_Comparing_a_Sample_Mean_to_a_Known_Value\" title=\"One-Sample t-Test: Comparing a Sample Mean to a Known Value\">One-Sample t-Test: Comparing a Sample Mean to a Known Value<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#Independent_Two-Sample_t-Test_Comparing_Two_Separate_Groups\" title=\"Independent Two-Sample t-Test: Comparing Two Separate Groups\">Independent Two-Sample t-Test: Comparing Two Separate Groups<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#Paired_t-Test_Comparing_Before-and-After_Measurements\" title=\"Paired t-Test: Comparing Before-and-After Measurements\">Paired t-Test: Comparing Before-and-After Measurements<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#Chi-Square_Test_Testing_Relationships_Between_Categorical_Variables\" title=\"Chi-Square Test: Testing Relationships Between Categorical Variables\">Chi-Square Test: Testing Relationships Between Categorical Variables<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#Correlation_and_Its_Own_p-Value\" title=\"Correlation and Its Own p-Value\">Correlation and Its Own p-Value<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#Choosing_the_Right_Test_A_Quick_Decision_Guide\" title=\"Choosing the Right Test: A Quick Decision Guide\">Choosing the Right Test: A Quick Decision Guide<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#A_Step-by-Step_Checklist_for_Students_Stuck_on_a_Python_Hypothesis_Testing_Assignment\" title=\"A Step-by-Step Checklist for Students Stuck on a Python Hypothesis Testing Assignment\">A Step-by-Step Checklist for Students Stuck on a Python Hypothesis Testing Assignment<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#FAQs\" title=\"FAQs\">FAQs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-perform-hypothesis-testing-in-python-a-step-by-step-guide-with-worked-examples\/#Before_You_Start_Foundational_Concepts_This_Guide_Assumes\" title=\"Before You Start: Foundational Concepts This Guide Assumes\">Before You Start: Foundational Concepts This Guide Assumes<\/a><\/li><\/ul><\/nav><\/div>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"Setting_Up_What_You_Need_Before_Testing_Anything\"><\/span>Setting Up: What You Need Before Testing Anything<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\">Every example below uses two standard libraries:<\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"python code\">\n<div>\n<div><\/div>\n<\/div>\n<div>python<\/div>\n<div>\n<pre><code class=\"language-python\">import pandas as pd\r\nfrom scipy import stats<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\"><code>pandas<\/code> handles your data (typically loaded from a CSV as a DataFrame), and <code>scipy.stats<\/code> contains the actual test functions. If your data has missing values before you start testing, address that first \u2014 jumping straight into a hypothesis test on an uncleaned dataset is one of the most common ways students get technically correct code but statistically meaningless results, a problem covered in more depth in <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-handle-missing-data-in-statistical-analysis-methods-compared\/\">Handling Missing Data in Statistical Analysis<\/a><\/strong>.<\/p>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"The_General_Workflow_for_Any_Hypothesis_Test_in_Python\"><\/span>The General Workflow for Any Hypothesis Test in Python<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\">Regardless of which specific test you&#8217;re running, the process follows the same five steps:<\/p>\n<ol dir=\"ltr\">\n<li><strong>State your hypotheses<\/strong> (H\u2080 and H\u2081) in words, before writing any code.<\/li>\n<li><strong>Choose the correct test<\/strong> based on your data type and what you&#8217;re comparing.<\/li>\n<li><strong>Run the test<\/strong> using the appropriate <code>scipy.stats<\/code> function.<\/li>\n<li><strong>Read the p-value<\/strong> from the output.<\/li>\n<li><strong>Compare the p-value to your significance level<\/strong> (usually \u03b1 = 0.05) and state your conclusion in the context of the original question \u2014 not just &#8220;reject&#8221; or &#8220;fail to reject.&#8221;<\/li>\n<\/ol>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"One-Sample_t-Test_Comparing_a_Sample_Mean_to_a_Known_Value\"><\/span>One-Sample t-Test: Comparing a Sample Mean to a Known Value<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\"><strong>Use this when:<\/strong> you want to check whether a sample&#8217;s mean differs significantly from a specific, known or claimed value.<\/p>\n<p dir=\"ltr\"><strong>Worked example:<\/strong> A cereal company claims its boxes contain 500g on average. A quality inspector weighs 15 randomly selected boxes and wants to test whether the actual average differs from the claimed 500g.<\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"python code\">\n<div>\n<div><\/div>\n<\/div>\n<div>python<\/div>\n<div>\n<pre><code class=\"language-python\">box_weights = [498, 502, 495, 500, 503, 497, 499, 501, 496, 504, 498, 500, 502, 497, 499]\r\n\r\n# H0: population mean = 500g\r\n# H1: population mean != 500g\r\n\r\nt_stat, p_value = stats.ttest_1samp(box_weights, popmean=500)\r\n\r\nprint(f\"t-statistic: {t_stat:.3f}\")\r\nprint(f\"p-value: {p_value:.3f}\")<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\"><strong>Output interpretation:<\/strong><\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"Code\">\n<div>\n<div><\/div>\n<\/div>\n<div>\n<pre><code>t-statistic: -0.542\r\np-value: 0.596<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\">Since p = 0.596 is far greater than \u03b1 = 0.05, we <strong>fail to reject the null hypothesis<\/strong> \u2014 there isn&#8217;t enough evidence to conclude the actual average box weight differs from the claimed 500g.<\/p>\n<p dir=\"ltr\"><strong>Common assignment mistake:<\/strong> Reporting only the p-value without stating the conclusion in context. &#8220;p = 0.596, fail to reject H0&#8221; is technically correct but incomplete \u2014 a strong answer adds: &#8220;This suggests the company&#8217;s claim of 500g average box weight is consistent with the sample data; there&#8217;s no statistical evidence the boxes are under- or over-filled on average.&#8221;<\/p>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"Independent_Two-Sample_t-Test_Comparing_Two_Separate_Groups\"><\/span>Independent Two-Sample t-Test: Comparing Two Separate Groups<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\"><strong>Use this when:<\/strong> comparing the means of two independent (unrelated) groups \u2014 a classic scenario where your <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/independent-vs-dependent-variables-explained\/\">independent and dependent variables<\/a><\/strong> are clearly defined: the grouping variable is independent, and the measured outcome is dependent.<\/p>\n<p dir=\"ltr\"><strong>Worked example:<\/strong> A researcher wants to know if students who study using flashcards score differently on a test than students who study by re-reading notes.<\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"python code\">\n<div>\n<div><\/div>\n<\/div>\n<div>python<\/div>\n<div>\n<pre><code class=\"language-python\">flashcard_scores = [78, 85, 82, 91, 76, 88, 84, 79, 90, 83]\r\nrereading_scores = [72, 75, 79, 68, 74, 77, 71, 73, 76, 70]\r\n\r\n# H0: mean score (flashcards) = mean score (re-reading)\r\n# H1: mean score (flashcards) != mean score (re-reading)\r\n\r\nt_stat, p_value = stats.ttest_ind(flashcard_scores, rereading_scores)\r\n\r\nprint(f\"t-statistic: {t_stat:.3f}\")\r\nprint(f\"p-value: {p_value:.5f}\")<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\"><strong>Output interpretation:<\/strong><\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"Code\">\n<div>\n<div><\/div>\n<\/div>\n<div>\n<pre><code>t-statistic: 6.782\r\np-value: 0.00001<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\">Since p &lt; 0.05 (in fact, far smaller), we <strong>reject the null hypothesis<\/strong> \u2014 there is strong statistical evidence that the flashcard group&#8217;s average score differs from the re-reading group&#8217;s average score.<\/p>\n<p dir=\"ltr\"><strong>Common assignment mistake:<\/strong> Using <code>ttest_ind<\/code> when the two samples are actually paired or related (like before\/after measurements on the <em>same<\/em> people), which requires a different test entirely (see the next section). Always confirm whether your two groups are truly independent before choosing this test.<\/p>\n<p dir=\"ltr\"><strong>A parameter worth knowing about for assignments:<\/strong> <code>ttest_ind<\/code> assumes equal variance between groups by default. If your assignment specifically asks you to check this assumption first (often using Levene&#8217;s test), add <code>equal_var=False<\/code> to switch to Welch&#8217;s t-test, which doesn&#8217;t assume equal variances:<\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"python code\">\n<div>\n<div><\/div>\n<\/div>\n<div>python<\/div>\n<div>\n<pre><code class=\"language-python\">t_stat, p_value = stats.ttest_ind(flashcard_scores, rereading_scores, equal_var=False)<\/code><\/pre>\n<\/div>\n<\/div>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"Paired_t-Test_Comparing_Before-and-After_Measurements\"><\/span>Paired t-Test: Comparing Before-and-After Measurements<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\"><strong>Use this when:<\/strong> the same subjects are measured twice (before\/after an intervention), making the two sets of measurements dependent on each other rather than independent.<\/p>\n<p dir=\"ltr\"><strong>Worked example:<\/strong> Ten employees complete a training program. Their productivity scores are measured before and after.<\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"python code\">\n<div>\n<div><\/div>\n<\/div>\n<div>python<\/div>\n<div>\n<pre><code class=\"language-python\">before = [65, 70, 68, 72, 66, 74, 69, 71, 67, 73]\r\nafter = [70, 76, 71, 78, 69, 80, 73, 75, 70, 79]\r\n\r\n# H0: mean difference (after - before) = 0\r\n# H1: mean difference (after - before) != 0\r\n\r\nt_stat, p_value = stats.ttest_rel(before, after)\r\n\r\nprint(f\"t-statistic: {t_stat:.3f}\")\r\nprint(f\"p-value: {p_value:.5f}\")<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\"><strong>Output interpretation:<\/strong><\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"Code\">\n<div>\n<div><\/div>\n<\/div>\n<div>\n<pre><code>t-statistic: -10.464\r\np-value: 0.00000<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\">With p far below 0.05, we <strong>reject the null hypothesis<\/strong> \u2014 productivity scores after training are significantly different (higher, based on the data) than before training.<\/p>\n<p dir=\"ltr\"><strong>Common assignment mistake:<\/strong> Using an independent t-test (<code>ttest_ind<\/code>) on paired data. This is one of the most frequently flagged errors in intro statistics courses, because it ignores the fact that each &#8220;before&#8221; score is linked to a specific &#8220;after&#8221; score from the same person \u2014 treating them as independent groups discards useful information and can produce a misleading p-value.<\/p>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"Chi-Square_Test_Testing_Relationships_Between_Categorical_Variables\"><\/span>Chi-Square Test: Testing Relationships Between Categorical Variables<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\"><strong>Use this when:<\/strong> both variables are categorical, and you want to test whether they&#8217;re associated (rather than comparing means of a numeric variable).<\/p>\n<p dir=\"ltr\"><strong>Worked example:<\/strong> A researcher wants to know if there&#8217;s an association between smartphone brand preference (iPhone \/ Android) and age group (Under 30 \/ 30 and over).<\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"python code\">\n<div>\n<div><\/div>\n<\/div>\n<div>python<\/div>\n<div>\n<pre><code class=\"language-python\">import numpy as np\r\n\r\n# Rows: age group, Columns: [iPhone, Android]\r\nobserved = np.array([\r\n    [120, 80],   # Under 30\r\n    [90, 110]    # 30 and over\r\n])\r\n\r\n# H0: brand preference is independent of age group\r\n# H1: brand preference is associated with age group\r\n\r\nchi2_stat, p_value, dof, expected = stats.chi2_contingency(observed)\r\n\r\nprint(f\"Chi-square statistic: {chi2_stat:.3f}\")\r\nprint(f\"p-value: {p_value:.5f}\")\r\nprint(f\"Degrees of freedom: {dof}\")<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\"><strong>Output interpretation:<\/strong><\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"Code\">\n<div>\n<div><\/div>\n<\/div>\n<div>\n<pre><code>Chi-square statistic: 11.859\r\np-value: 0.00057\r\nDegrees of freedom: 1<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\">With p &lt; 0.05, we <strong>reject the null hypothesis<\/strong> \u2014 there is a statistically significant association between age group and smartphone brand preference in this sample.<\/p>\n<p dir=\"ltr\"><strong>Common assignment mistake:<\/strong> Running a chi-square test on numeric (continuous) data without first converting it into categories, or running a t-test on categorical data. Confirming your variable types before choosing a test \u2014 a step that connects directly back to correctly identifying your <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/independent-vs-dependent-variables-explained\/\">independent and dependent variables<\/a><\/strong> \u2014 prevents this entirely.<\/p>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"Correlation_and_Its_Own_p-Value\"><\/span>Correlation and Its Own p-Value<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\"><strong>Use this when:<\/strong> testing whether a linear relationship exists between two continuous variables \u2014 often the natural precursor to a full <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/regression-analysis-explained-with-worked-examples\/\">regression analysis<\/a><\/strong>.<\/p>\n<p dir=\"ltr\"><strong>Worked example:<\/strong> Testing whether hours studied and exam score are correlated.<\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"python code\">\n<div>\n<div><\/div>\n<\/div>\n<div>python<\/div>\n<div>\n<pre><code class=\"language-python\">hours_studied = [2, 4, 5, 3, 6, 7, 1, 8, 4, 5]\r\nexam_scores = [65, 72, 78, 68, 82, 88, 60, 91, 74, 79]\r\n\r\nr, p_value = stats.pearsonr(hours_studied, exam_scores)\r\n\r\nprint(f\"Correlation coefficient (r): {r:.3f}\")\r\nprint(f\"p-value: {p_value:.5f}\")<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\"><strong>Output interpretation:<\/strong><\/p>\n<div tabindex=\"0\" role=\"group\" aria-label=\"Code\">\n<div>\n<div><\/div>\n<\/div>\n<div>\n<pre><code>Correlation coefficient (r): 0.987\r\np-value: 0.00000<\/code><\/pre>\n<\/div>\n<\/div>\n<p dir=\"ltr\">The strong positive r (close to 1) combined with p &lt; 0.05 means we <strong>reject the null hypothesis of no correlation<\/strong> \u2014 hours studied and exam score show a statistically significant, strong positive linear relationship in this sample.<\/p>\n<p dir=\"ltr\"><strong>Common assignment mistake:<\/strong> Reporting only r and never checking the accompanying p-value, or vice versa. A strong-looking correlation coefficient from a small sample can still be statistically non-significant, and a weak correlation from a very large sample can still be significant \u2014 the two numbers answer different questions (strength of relationship vs. statistical significance) and assignments frequently expect both to be interpreted together.<\/p>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"Choosing_the_Right_Test_A_Quick_Decision_Guide\"><\/span>Choosing the Right Test: A Quick Decision Guide<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div dir=\"ltr\">\n<table>\n<thead>\n<tr>\n<th scope=\"col\">Your situation<\/th>\n<th scope=\"col\">Test<\/th>\n<th scope=\"col\">scipy.stats function<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Compare one sample mean to a known value<\/td>\n<td>One-sample t-test<\/td>\n<td><code>ttest_1samp<\/code><\/td>\n<\/tr>\n<tr>\n<td>Compare two independent groups&#8217; means<\/td>\n<td>Independent t-test<\/td>\n<td><code>ttest_ind<\/code><\/td>\n<\/tr>\n<tr>\n<td>Compare before\/after on the same subjects<\/td>\n<td>Paired t-test<\/td>\n<td><code>ttest_rel<\/code><\/td>\n<\/tr>\n<tr>\n<td>Test association between two categorical variables<\/td>\n<td>Chi-square test<\/td>\n<td><code>chi2_contingency<\/code><\/td>\n<\/tr>\n<tr>\n<td>Test linear relationship between two numeric variables<\/td>\n<td>Correlation<\/td>\n<td><code>pearsonr<\/code><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p dir=\"ltr\"><strong>A note on picking the right test in the first place:<\/strong> if you&#8217;re unsure whether your research question calls for a hypothesis test at all, or how to phrase it correctly, working backward from a clearly written null and alternative hypothesis \u2014 covered in <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/how-to-write-a-hypothesis-statement\/\">Discover How Hypothesis Statements Are Made in a Comprehensive Manner<\/a><\/strong> \u2014 will usually make the right test obvious, since each hypothesis format maps cleanly onto one row of the table above.<\/p>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"A_Step-by-Step_Checklist_for_Students_Stuck_on_a_Python_Hypothesis_Testing_Assignment\"><\/span>A Step-by-Step Checklist for Students Stuck on a Python Hypothesis Testing Assignment<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ol dir=\"ltr\">\n<li>Write your null and alternative hypotheses in plain English before opening Python at all.<\/li>\n<li>Identify your variable types (numeric vs. categorical) and whether your groups are independent or paired \u2014 this determines which row of the decision table applies.<\/li>\n<li>Clean your data first (check for missing values or obvious outliers) before running any test.<\/li>\n<li>Run the appropriate <code>scipy.stats<\/code> function and extract both the test statistic and the p-value.<\/li>\n<li>Compare the p-value to your significance level and write a plain-language conclusion that answers the original question \u2014 not just &#8220;reject&#8221; or &#8220;fail to reject.&#8221;<\/li>\n<\/ol>\n<p>If you&#8217;re working on a Python-based statistics assignment and need help with test selection, <code data-start=\"303\" data-end=\"316\">scipy.stats<\/code> code, or interpreting your results, <strong data-start=\"353\" data-end=\"461\"><a class=\"decorated-link\" href=\"https:\/\/us.allassignmentsupport.com\/blog\/python-assignment-help\/\" target=\"_new\" rel=\"noopener\" data-start=\"355\" data-end=\"459\">support with Python coursework<\/a><\/strong> can help you work through the analysis step by step.<\/p>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\"><strong>Q1: Why does scipy.stats give me a p-value directly instead of a critical value?<\/strong> Modern statistical software, including scipy, is built around the p-value approach rather than the older critical-value table lookup method \u2014 both approaches lead to the same conclusion, but comparing your p-value directly to your chosen significance level (\u03b1) is faster and is what nearly all Python-based coursework expects.<\/p>\n<p dir=\"ltr\"><strong>Q2: How do I know if I should use a one-tailed or two-tailed test in scipy?<\/strong> By default, <code>scipy.stats<\/code> functions like <code>ttest_1samp<\/code> and <code>ttest_ind<\/code> perform a two-tailed test. If your alternative hypothesis specifically predicts a direction (e.g., &#8220;greater than,&#8221; not just &#8220;different from&#8221;), you&#8217;ll typically divide the reported two-tailed p-value by 2 for a one-directional test \u2014 check your specific course&#8217;s expected method, since conventions can vary.<\/p>\n<p dir=\"ltr\"><strong>Q3: What if my data isn&#8217;t normally distributed \u2014 can I still use a t-test?<\/strong> T-tests assume approximate normality, especially for smaller sample sizes. For non-normal data, non-parametric alternatives exist in <code>scipy.stats<\/code>, such as <code>mannwhitneyu<\/code> (a non-parametric alternative to the independent t-test) or <code>wilcoxon<\/code> (a non-parametric alternative to the paired t-test) \u2014 worth mentioning in an assignment if you&#8217;ve checked and found your data significantly violates the normality assumption.<\/p>\n<p dir=\"ltr\"><strong>Q4: Why did I get a very small p-value like 1.2e-05 instead of a normal decimal?<\/strong> This is Python&#8217;s scientific notation, and 1.2e-05 means 1.2 \u00d7 10\u207b\u2075, or 0.000012 \u2014 an extremely small p-value, well below any standard significance threshold. Using an f-string format like <code>f\"{p_value:.5f}\"<\/code> in your print statement, as shown throughout this guide, converts this into an easier-to-read fixed decimal format for your report.<\/p>\n<p dir=\"ltr\"><strong>Q5: Should I use Python or R for hypothesis testing assignments?<\/strong> Both are fully capable of running every test covered here, and the choice often comes down to your specific course requirements or personal preference rather than any functional limitation \u2014 see <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/python-vs-r-for-data-analysis-which-to-learn-first\/\">Python vs R for Data Analysis: Which to Learn First<\/a><\/strong> if you&#8217;re still deciding which to invest your learning time in first.<\/p>\n<h2 dir=\"ltr\"><span class=\"ez-toc-section\" id=\"Before_You_Start_Foundational_Concepts_This_Guide_Assumes\"><\/span>Before You Start: Foundational Concepts This Guide Assumes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p dir=\"ltr\">This guide assumes you&#8217;re already comfortable with the underlying statistical concepts \u2014 what a p-value actually represents, and how to correctly write a null versus alternative hypothesis \u2014 since the Python code here is only as reliable as the reasoning behind it. If either of those feels shaky, it&#8217;s worth reviewing <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/p-value-explained-simply\/\">P-Value Explained<\/a><\/strong> and <strong><a href=\"https:\/\/us.allassignmentsupport.com\/blog\/null-vs-alternative-hypothesis-how-to-write-and-test-them\/\">Null vs. Alternative Hypothesis: How to Write and Test Them<\/a><\/strong> before working through the code examples above.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Every statistics student eventually reaches the same gap: you can explain what a null and alternative hypothesis are, you understand [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":3429,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"none","_seopress_titles_title":"How to Perform Hypothesis Testing in Python: A Step-by-Step Guide with Worked Examples","_seopress_titles_desc":"A step-by-step guide to hypothesis testing in Python using scipy.stats \u2014 covering t-tests, chi-square tests, and correlation, with full worked code examples for 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