{"id":33609,"date":"2021-10-07T07:13:30","date_gmt":"2021-10-07T07:13:30","guid":{"rendered":"https:\/\/analystprep.com\/cfa-level-1-exam\/?p=33609"},"modified":"2026-01-23T10:31:40","modified_gmt":"2026-01-23T10:31:40","slug":"decision-rules-in-hypothesis-tests","status":"publish","type":"post","link":"https:\/\/analystprep.com\/cfa-level-1-exam\/quantitative-methods\/decision-rules-in-hypothesis-tests\/","title":{"rendered":"Decision Rules in Hypothesis Tests"},"content":{"rendered":"\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"VideoObject\",\n  \"name\": \"Hypothesis Testing (2025 Level I CFA\u00ae Exam \u2013 Quantitative Methods \u2013 Module 6)\",\n  \"description\": \"This lesson covers hypothesis testing for the 2025 CFA\u00ae Level I Quantitative Methods curriculum. It explains the steps of hypothesis testing, null and alternative hypotheses, one-tailed and two-tailed tests, test statistics, Type I and Type II errors, significance levels, and test power. The video also interprets p-values, confidence intervals, and economic versus statistical significance, and applies appropriate test statistics for means, mean differences, variances, correlations, and contingency table data, including comparisons of parametric and nonparametric tests.\",\n  \"uploadDate\": \"2021-11-18T00:00:00+00:00\",\n  \"thumbnailUrl\": \"https:\/\/img.youtube.com\/vi\/twTTWMuEGx8\/default.jpg\",\n  \"contentUrl\": \"https:\/\/youtu.be\/twTTWMuEGx8\",\n  \"embedUrl\": \"https:\/\/www.youtube.com\/embed\/twTTWMuEGx8\",\n  \"duration\": \"PT58M47S\"\n}\n<\/script>\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"QAPage\",\n  \"mainEntity\": {\n    \"@type\": \"Question\",\n    \"name\": \"Use data from the previous example to carry out a test at 5% significance to determine whether the average IQ of candidates is greater than 102.\",\n    \"text\": \"Use data from the previous example to carry out a test at 5% significance to determine whether the average IQ of candidates is greater than 102.\",\n    \"answerCount\": 3,\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"The correct answer is B. There is insufficient evidence to justify rejecting the null hypothesis at the 5% level of significance. The hypothesis test is H0: \u03bc = 102 versus H1: \u03bc > 102. The test statistic is (105 \u2212 102) \/ (20 \/ \u221a50) = 1.061. Because this value is less than the critical value of 1.6449 for a right-tailed test at the 5% significance level, the null hypothesis cannot be rejected. Therefore, it is reasonable to conclude that the average IQ is not greater than 102.\"\n    }\n  }\n}\n<\/script>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"ImageObject\",\n  \"url\": \"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-171.jpg\",\n  \"caption\": \"Decision rules in hypothesis testing\",\n  \"width\": 1463,\n  \"height\": 765,\n  \"copyrightNotice\": \"\u00a9 2024 AnalystPrep\",\n  \"acquireLicensePage\": \"https:\/\/analystprep.com\/license-info\",\n  \"creditText\": \"AnalystPrep Design Team\",\n  \"creator\": {\n    \"@type\": \"Organization\",\n    \"name\": \"AnalystPrep\"\n  }\n}\n<\/script>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"ImageObject\",\n  \"url\": \"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172b-768x402.jpg\",\n  \"caption\": \"Critical values and rejection regions in hypothesis testing\",\n  \"width\": 768,\n  \"height\": 402,\n  \"copyrightNotice\": \"\u00a9 2024 AnalystPrep\",\n  \"acquireLicensePage\": \"https:\/\/analystprep.com\/license-info\",\n  \"creditText\": \"AnalystPrep Design Team\",\n  \"creator\": {\n    \"@type\": \"Organization\",\n    \"name\": \"AnalystPrep\"\n  }\n}\n<\/script>\n\n\n\n<iframe loading=\"lazy\"\n  width=\"611\"\n  height=\"344\"\n  src=\"https:\/\/www.youtube.com\/embed\/twTTWMuEGx8\"\n  title=\"YouTube video player\"\n  frameborder=\"0\"\n  allow=\"accelerometer; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\"\n  referrerpolicy=\"strict-origin-when-cross-origin\"\n  allowfullscreen>\n<\/iframe>\n\n\n\n<p>The decision rule refers to the procedure followed by analysts and researchers when determining whether to reject or not to reject a null hypothesis. We use the phrase \u201cnot to reject\u201d because it is considered statistically incorrect to \u201caccept\u201d a null hypothesis. Rather, we can only assemble enough evidence to support it.<\/p>\n\n\n\n<!--more-->\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Breaking Down the Decision Rule<\/strong><\/h2>\n\n\n\n<p>The decision to either reject or not to reject a null hypothesis is guided by the distribution the test statistic assumes. This means that if the variable involved follows a normal distribution, we use the level of significance of the test to come up with critical values that lie along the standard normal distribution.<\/p>\n\n\n\n<p>Note that before one makes a decision to reject or not to reject a null hypothesis, one must consider whether the test should be one-tailed or two-tailed. This is because the number of tails determines the value of \u03b1 (significance level). The following is a summary of the decision rules under different scenarios.<\/p>\n\n\n\n<div style=\"margin: 0 0 20px 0;\">\n  <a\n    href=\"https:\/\/analystprep.com\/free-trial\/\"\n    target=\"_blank\"\n    rel=\"noopener noreferrer\"\n    style=\"\n      display: inline-block;\n      border: 2px solid #1e63ff;\n      color: #1e63ff;\n      background: #ffffff;\n      padding: 10px 14px;\n      border-radius: 10px;\n      font-weight: 500;\n      line-height: 1.35;\n      text-decoration: none;\n    \"\n  >\n    Want to practice hypothesis test decision rules using CFA-style scenarios? Try AnalystPrep\u2019s free trial now.\n  <\/a>\n<\/div>\n\n\n<\/p>\n<h3><strong>Left One-tailed Test<\/strong><\/h3>\n<p>H<sub>1<\/sub>: Parameter &lt; X<\/p>\n<p>Decision rule: Reject H<sub>0<\/sub> if the test statistic is less than the critical value. Otherwise, <strong>do not reject<\/strong> H<sub>0.<\/sub><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-17059\" src=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-171.jpg\" alt=\"decision-rule-left-one-tailed-test\" width=\"1463\" height=\"765\" srcset=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-171.jpg 1463w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-171-300x157.jpg 300w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-171-768x402.jpg 768w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-171-1024x535.jpg 1024w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-171-400x209.jpg 400w\" sizes=\"auto, (max-width: 1463px) 100vw, 1463px\" \/><\/p>\n<h3><strong>Right One-tailed Test<\/strong><\/h3>\n<p>H<sub>1<\/sub>: Parameter &gt; X<\/p>\n<p>Decision rule: Reject H<sub>0<\/sub> if the test statistic is greater than the critical value. Otherwise, <strong>do not reject<\/strong> H<sub>0.<\/sub><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-17061\" src=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172a.jpg\" alt=\"decision-rule-right-one-tailed-test\" width=\"1463\" height=\"765\" srcset=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172a.jpg 1463w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172a-300x157.jpg 300w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172a-768x402.jpg 768w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172a-1024x535.jpg 1024w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172a-400x209.jpg 400w\" sizes=\"auto, (max-width: 1463px) 100vw, 1463px\" \/><\/p>\n<h3><strong>Two-tailed Test<\/strong><\/h3>\n<p>H<sub>1<\/sub>: Parameter <em>\u2260<\/em> X (not equal to X)<\/p>\n<p>Decision rule: Reject H<sub>0<\/sub> if the test statistic is greater than the upper critical value or less than the lower critical value.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-17062\" src=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172b.jpg\" alt=\"decision-rule-two-tailed-test\" width=\"1463\" height=\"765\" srcset=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172b.jpg 1463w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172b-300x157.jpg 300w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172b-768x402.jpg 768w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172b-1024x535.jpg 1024w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-172b-400x209.jpg 400w\" sizes=\"auto, (max-width: 1463px) 100vw, 1463px\" \/><\/p>\n<h2><strong>The Link Between Confidence Interval and Hypothesis Testing<\/strong><\/h2>\n<p>Critical values link confidence intervals to hypothesis tests. For example, to construct a 95% confidence interval assuming a normal distribution, we would need to determine the critical values that correspond to a 5% significance level. Similarly, if we were to conduct a test of some given hypothesis at the 5% significance level, we would use the same critical values used for the confidence interval to subdivide the distribution space into rejection and non-rejection regions.<\/p>\n<h3><strong>Example: Hypothesis Testing<\/strong><\/h3>\n<p>A survey carried out using a sample of 50 Level I candidates reveals an average IQ of 100. Assuming that IQs are distributed normally, carry out a statistical test to determine whether the mean IQ is greater than 105. You are instructed to use a 5% level of significance. (Previous studies give a standard deviation of IQs of approximately 20.)<\/p>\n<p><strong>Solution<\/strong><\/p>\n<p>First, state the hypothesis:<\/p>\n<p>H<sub>0<\/sub>: \u03bc = 105 vs H<sub>1<\/sub>: \u03bc &gt; 105<\/p>\n<p>Since IQs follow a normal distribution, under \\(H_0, \\frac {(X&#8217; &#8211; 100)}{\\left( \\frac {\\sigma}{\\sqrt n} \\right)} \\sim N(0,1)\\).<\/p>\n<p>Next, we compute the test statistic, which is \\(\\frac {(105 \u2013 100)}{\\left(\\frac {20}{\\sqrt {50}} \\right)} = 1.768\\).<\/p>\n<p>This is a right one-tailed test, and IQs are distributed normally. Therefore, we should compare our test statistic to the upper 5% point of the normal distribution.<\/p>\n<p>From the normal distribution table, this value is 1.6449. Since 1.768 is greater than 1.6449, we have <strong>sufficient evidence<\/strong> to reject the H<sub>0<\/sub> at the 5% significance level. Therefore, it is <strong>reasonable <\/strong>to conclude that the mean IQ of CFA candidates is greater than 100.<\/p>\n<p>(<em>Note the choice of words used in the decision-making part and the conclusion.)<\/em><\/p>\n<h2><strong>Statistical Significance vs. Economic Significance<\/strong><\/h2>\n<p><strong><span data-preserver-spaces=\"true\">Statistical significance\u00a0<\/span><\/strong><span data-preserver-spaces=\"true\">refers to the use of a sample to carry out a statistical test meant to reveal any significant deviation from the stated null hypothesis. We then decide whether to reject or not reject the null hypothesis.<\/span><\/p>\n<p><strong><span data-preserver-spaces=\"true\">Economic significance entails the statistical significance and<\/span><\/strong><span data-preserver-spaces=\"true\">\u00a0the economic effect inherent in the decision made after data analysis and testing.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">The need to separate statistical significance from economic significance arises because some statistical results may be significant on paper but not economically meaningful. The difference from the hypothesized value may carry some statistical weight but lack economic feasibility, making implementation of the results very unlikely. Perhaps an example can help you gain a deeper understanding of the two concepts.<\/span><\/p>\n<h4><strong>Example: Statistical Significance and Economic Feasibility\u00a0<\/strong><\/h4>\n<p><span data-preserver-spaces=\"true\">A well-established pharmaceutical company wishes to assess the effectiveness of a newly developed drug before commercialization. The company&#8217;s board of directors commissions a pilot test. The drug is administered to a few patients to whom none of the existing drugs has been prescribed. A statistical test follows and reveals a significant decrease in the average number of days taken before full recovery. The company considers the evidence sufficient to conclude that the new drug is more effective than existing alternatives.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">However, the production of the new drug is significantly more expensive because of the scarcity of the active ingredient. Furthermore, the company would have to engage in a year-long lobbying exercise to convince the Food and Drug Administration and the general public that the drug is indeed an improvement to the existing brands. At the end of the day, the management decides to delay the commercialization of the drug because of the higher production and introduction costs.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\">Other factors that may affect the economic feasibility of statistical results include:<\/span><\/p>\n<ul>\n<li><strong><span data-preserver-spaces=\"true\">Tax<\/span><\/strong><span data-preserver-spaces=\"true\">: Financial institutions generally avoid projects that may increase the tax payable.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\"><strong>Shareholders: <\/strong>They are often trying to increase returns on investment from one year to the next, not taking into account the long run because their investment horizon is too short.<\/span><\/li>\n<li><strong><span data-preserver-spaces=\"true\">Risk<\/span><\/strong><span data-preserver-spaces=\"true\">: We may have a statistically significant project that is too risky. Projects that are capital intensive are, in the long term, particularly, very risky. In fact, the additional risk is excluded from statistical tests.<\/span><\/li>\n<\/ul>\n<p><span data-preserver-spaces=\"true\">Evidence of returns based solely on statistical analysis may not be enough to guarantee the implementation of a project. In particular, large samples may produce results that have high statistical significance but very low applicability.<\/span><\/p>\n<blockquote>\n<h2><strong>Question<\/strong><\/h2>\n<p>Use data from the previous example to carry out a test at 5% significance to determine whether the average IQ of candidates is greater than 102.<\/p>\n<ol style=\"list-style-type: upper-alpha;\">\n<li data-tadv-p=\"keep\">There is sufficient evidence to justify the rejection of the H<sub>0 <\/sub>and inform the conclusion that the average IQ is greater than 102.<\/li>\n<li data-tadv-p=\"keep\">There is insufficient evidence to justify the rejection of the H<sub>0 <\/sub>and guide the conclusion that the average IQ is not more than 102.<\/li>\n<li data-tadv-p=\"keep\">There is sufficient evidence to justify the rejection of the H<sub>0 <\/sub>and inform the conclusion that the average IQ is greater than 102.<\/li>\n<\/ol>\n<p><strong>Solution<\/strong><\/p>\n<p>The correct answer is <strong>B<\/strong>.<\/p>\n<p>Just like in the example above, start with the statement of the hypothesis;<\/p>\n<p>H<sub>0<\/sub>: \u03bc = 102 vs. H<sub>1<\/sub>: \u03bc &gt; 102<\/p>\n<p>The test statistic is \\(\\frac {(105 \u2013 102)}{\\left( \\frac {20}{\\sqrt{50}} \\right)} = 1.061\\).<\/p>\n<p>Again, this is a right one-tailed test but this time, 1.061 is less than the upper 5% point of a standard normal distribution (1.6449). Therefore, we <strong>do not have sufficient evidence<\/strong> to reject the H<sub>0<\/sub> at the 5% level of significance. It is, therefore, reasonable to conclude that the average IQ of CFA candidates <strong>is not more than<\/strong> 102.<\/p>\n<\/blockquote>\n<div class=\"notes_inv\">\n<hr \/><\/div>\n\n\n<div style=\"text-align: center; margin: 32px 0;\">\n  <a\n    href=\"https:\/\/analystprep.com\/free-trial\/\"\n    target=\"_blank\"\n    rel=\"noopener noreferrer\"\n    style=\"\n      display: inline-block;\n      background-color: #1e63ff;\n      color: #ffffff;\n      padding: 12px 26px;\n      border-radius: 12px;\n      font-weight: 600;\n      font-size: 16px;\n      text-decoration: none;\n    \"\n  >\n    Start Free Trial \u2192\n  <\/a>\n\n  <div style=\"margin-top: 10px; font-size: 14px; color: #374151;\">\n    Practice hypothesis testing, p-values, critical values, and Type I\/II error decisions with full CFA\u00ae-style solutions.\n  <\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>The decision rule refers to the procedure followed by analysts and researchers when determining whether to reject or not to reject a null hypothesis. We use the phrase \u201cnot to reject\u201d because it is considered statistically incorrect to \u201caccept\u201d a&#8230;<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2],"tags":[],"class_list":["post-33609","post","type-post","status-publish","format-standard","hentry","category-quantitative-methods","blog-post","no-post-thumbnail","animate"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Decision Rules in Hypothesis Testing | CFA Level 1<\/title>\n<meta name=\"description\" content=\"A decision rule in hypothesis testing determines when to reject or fail to reject the null hypothesis. 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