{"id":25221,"date":"2021-08-12T13:22:25","date_gmt":"2021-08-12T13:22:25","guid":{"rendered":"https:\/\/analystprep.com\/cfa-level-1-exam\/?p=25221"},"modified":"2026-04-27T07:07:19","modified_gmt":"2026-04-27T07:07:19","slug":"interpreting-skewness","status":"publish","type":"post","link":"https:\/\/analystprep.com\/cfa-level-1-exam\/quantitative-methods\/interpreting-skewness\/","title":{"rendered":"Interpreting Skewness"},"content":{"rendered":"\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"VideoObject\",\n  \"name\": \"Organizing, Visualizing, and Describing Data \u2013 Part II: Measures of Central Tendency (Level I CFA)\",\n  \"description\": \"This CFA Level 1 video lesson covers measures of central tendency, dispersion, skewness, kurtosis, quantiles, and correlation, including calculations, interpretations, and their applications in investment analysis and data visualization.\",\n  \"uploadDate\": \"2021-10-08T00:00:00+00:00\",\n  \"thumbnailUrl\": \"https:\/\/img.youtube.com\/vi\/M0gKgPztSoM\/maxresdefault.jpg\",\n  \"contentUrl\": \"https:\/\/youtu.be\/M0gKgPztSoM\",\n  \"embedUrl\": \"https:\/\/www.youtube.com\/embed\/M0gKgPztSoM\",\n  \"duration\": \"PT41M38S\",\n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"AnalystPrep\",\n    \"logo\": {\n      \"@type\": \"ImageObject\",\n      \"url\": \"https:\/\/analystprep.com\/wp-content\/uploads\/logo.png\",\n      \"width\": 250,\n      \"height\": 37\n    }\n  },\n  \"url\": \"https:\/\/analystprep.com\/cfa-level-1-exam\/\"\n}\n<\/script>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"ImageObject\",\n  \"url\": \"https:\/\/cdn.analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/05082832\/page-601.jpg\",\n  \"caption\": \"Image Showing Symmetrical Distribution\",\n  \"width\": 1463,\n  \"height\": 972,\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:\/\/cdn.analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/05082849\/page-61a.jpg\",\n  \"caption\": \"Image showing positively skewed distribution\",\n  \"width\": 1373,\n  \"height\": 860,\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\/2023\/07\/Img_1-6.jpg\",\n  \"caption\": \"Image showing negatively skewed distribution\",\n  \"width\": 1590,\n  \"height\": 996,\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\" width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/M0gKgPztSoM?si=NKZlQ763YgvxQm56\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n\n\n\n<p>Skewness refers to the degree of deviation from a symmetrical distribution, such as the normal distribution. A symmetrical distribution has identical shapes on either side of the mean.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"1463\" height=\"972\" src=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-601.jpg\" alt=\"Image Showing Symmetrical Distribution\" class=\"wp-image-16942\" srcset=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-601.jpg 1463w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-601-300x199.jpg 300w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-601-768x510.jpg 768w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-601-1024x680.jpg 1024w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-601-400x266.jpg 400w\" sizes=\"auto, (max-width: 1463px) 100vw, 1463px\" \/><\/figure>\n<\/div>\n\n\n<p>Distributions that are nonsymmetrical have unequal shapes on either side of the mean and lead us to skewness. This is because nonsymmetrical distributions depart from the usual bell shape of the normal distribution.<\/p>\n\n\n\n<p>Skewness can be positive, negative, or in some cases, undefined. The shape of a skewed distribution depends on outliers, which are extremely negative and positive observations.<\/p>\n\n\n\n<div style=\"text-align:center;margin:25px 0;\">\n  <a href=\"https:\/\/analystprep.com\/free-trial\/\" target=\"_blank\"\n     style=\"display:inline-block;padding:12px 20px;border:2px solid #2f5cff;border-radius:999px;\n            color:#2f5cff;text-decoration:none;background:#f7f9fc;white-space:nowrap;\">\n     Understand skewness faster with our free trial.\n  <\/a>\n<\/div>\n\n\n<h2><strong>Positive Skewness<\/strong><\/h2>\n<p>A positively skewed distribution has a<strong> long right tail<\/strong> because of many outliers or extreme values on the right side. Perhaps the best way to remember its shape is to consider its points in a positive direction. Most data points are concentrated on the right side.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-16943 size-full\" style=\"max-width: 100%;\" src=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61a.jpg\" alt=\"Image showing positively skewed distribution\" width=\"1373\" height=\"860\" srcset=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61a.jpg 1373w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61a-300x188.jpg 300w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61a-768x481.jpg 768w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61a-1024x641.jpg 1024w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61a-400x251.jpg 400w\" sizes=\"auto, (max-width: 1373px) 100vw, 1373px\" \/><\/p>\n<p>An example of a positively skewed distribution would be the income of individuals living in a certain country.<\/p>\n<h2><strong>Negative Skewness<br \/><\/strong><\/h2>\n<p>A negatively skewed distribution has a <strong>long left tail<\/strong> resulting from many outliers on the left side of the distribution. Therefore, we could say that it points in the negative direction. This is because the left side harbors most of the data points.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-16944 size-full\" style=\"max-width: 100%;\" src=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61B.jpg\" alt=\"Image showing negatively skewed distribution\" width=\"1373\" height=\"860\" srcset=\"https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61B.jpg 1373w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61B-300x188.jpg 300w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61B-768x481.jpg 768w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61B-1024x641.jpg 1024w, https:\/\/analystprep.com\/cfa-level-1-exam\/wp-content\/uploads\/2019\/08\/page-61B-400x251.jpg 400w\" sizes=\"auto, (max-width: 1373px) 100vw, 1373px\" \/><\/p>\n<p>A layman&#8217;s example of a negatively skewed distribution would be the number of fingers. Most people have 10 fingers, and only a few have less, possibly after losing some in an accident.<\/p>\n<h2><strong>Application of Skewness<\/strong><\/h2>\n<p>Skewness is particularly important in finance. Most market data either exhibit positive skewness or negative skewness, e.g., the prices of stocks and mortgage costs. Thus, an investor can estimate whether the future price will be less or greater than the mean by simply knowing the type of skewness adopted by the particular market segment involved.<\/p>\n\n\n<div style=\"text-align:center;margin:40px 0;\">\n  <a href=\"https:\/\/analystprep.com\/free-trial\/\" target=\"_blank\"\n     style=\"display:inline-block;padding:14px 26px;background:#4a76d1;color:#fff;border-radius:999px;text-decoration:none;\">\n     Start Free Trial \u2192\n  <\/a>\n  <p style=\"margin-top:10px;\">\n    Learn distribution shape and skewness with clear study tools.\n  <\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Skewness refers to the degree of deviation from a symmetrical distribution, such as the normal distribution. A symmetrical distribution has identical shapes on either side of the mean. Distributions that are nonsymmetrical have unequal shapes on either side of the&#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-25221","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 v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Interpreting Positive and Negative Skewness | CFA Level 1<\/title>\n<meta name=\"description\" content=\"Skewness is the degree of deviation from a symmetrical distribution, e.g., normal distribution. 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