{"id":1596,"date":"2019-08-27T17:37:00","date_gmt":"2019-08-27T17:37:00","guid":{"rendered":"https:\/\/analystprep.com\/cfa-level-1-exam\/?p=1596"},"modified":"2026-03-08T17:56:19","modified_gmt":"2026-03-08T17:56:19","slug":"sampling-considerations-common-biases","status":"publish","type":"post","link":"https:\/\/analystprep.com\/cfa-level-1-exam\/quantitative-methods\/sampling-considerations-common-biases\/","title":{"rendered":"Sampling Considerations and 5 Common Biases in Sampling"},"content":{"rendered":"\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"VideoObject\",\n  \"name\": \"Sampling and Estimation (2021 Level I CFA\u00ae Exam \u2013 Reading 10)\",\n  \"description\": \"This video lesson covers the fundamentals of sampling and estimation in quantitative methods. It explains simple and stratified random sampling, the central limit theorem, point estimators, confidence intervals, and the standard error. The lesson also highlights biases such as data mining, survivorship, and look-ahead bias that can affect sampling reliability.\",\n  \"uploadDate\": \"2019-12-18T00:00:00+00:00\",\n  \"thumbnailUrl\": \"https:\/\/i.ytimg.com\/vi\/mgY_3CHHYBw\/hqdefault.jpg\",\n  \"contentUrl\": \"https:\/\/www.youtube.com\/watch?v=mgY_3CHHYBw\",\n  \"embedUrl\": \"https:\/\/www.youtube.com\/embed\/mgY_3CHHYBw\",\n  \"duration\": \"PT32M00S\"\n}\n<\/script>\n\n\n\n<iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/mgY_3CHHYBw?si=GcBqbRAe3MIT7aJJ\" 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<p>Sampling considerations refer to the desirable characteristics that should always be taken into account when selecting a sample so as to increase chances of accurate estimation of population parameters. In general, larger samples are preferred to smaller ones because the confidence intervals that result from the use of a large n are narrower. Therefore, such intervals have more confidence and reliability. This means that the estimation of the population parameter is more precise compared to smaller samples.<\/p>\n<p><!--more--><\/p>\n<p>Further, a large sample may be desirable in any statistical analysis because the standard error is inversely proportional to the sample size, i.e., the standard error reduces when <em>n<\/em> increases.<\/p>\n<p>However, larger samples have the following shortcomings:<\/p>\n<ol>\n<li>some population parameters demonstrate a tendency to change over time, especially stock market financial performances which are affected by ever-changing factors. This means that mixing \u201cold\u201d data with more recent data may result in an unreliable and somewhat outdated population parameter; and<\/li>\n<li>taking a larger sample may increase the overall sampling costs.<\/li>\n<\/ol>\n<h2><strong>Sampling Considerations: 5 Common Biases in Sampling<\/strong><\/h2>\n<h3><strong>Data Mining Bias<\/strong><\/h3>\n<p>Data mining is the practice of analyzing historical data so as to unearth trends and other inherent relationships between variables. Analysts may then use such trends to predict future behavior.<\/p>\n<p>Data mining bias occurs when analysts excessively analyze data, giving rise to statistically irrelevant and, sometimes, non-existent trends.<\/p>\n<h3><strong>Sample Selection Bias<\/strong><\/h3>\n<p>Sample selection bias refers to the tendency to exclude a section of the population from sample analysis due to unavailability of data. This erodes the idea of randomness since the exclusion of a certain class of data somewhat amounts to collecting data from a subset of the population. The resulting parameter is, as such, not representative of the population as a whole.<\/p>\n<h3><strong>Survivorship Bias<\/strong><\/h3>\n<p>Survivorship bias entails exclusion of information that relates to financial vehicles that are no longer existent, during sampling. Consequently, conclusions may underestimate or overestimate the population parameters. For example, most mutual fund databases that track performance may exclude funds that have underperformed, leading to closure. Analyzing only the \u201csurviving\u201d funds may overestimate the average mutual fund earnings.<\/p>\n<h3><strong>Look-ahead Bias<\/strong><\/h3>\n<p>Look-ahead bias is occasioned by an analyst assumption that information is readily available on a certain date when in, fact, it\u2019s not. For example, analysts may assume that end-of-year financial information, such as the annual profit generated, is available in January yet most companies take up to 60 additional days before releasing results.<\/p>\n<h3><strong>Time Period Bias<\/strong><\/h3>\n<p>Time period bias involves inappropriate generalization of time-specific results \u2013 those results that only apply to certain seasons or periods. Most entities experience seasonal variation in performance so that some months may be more productive than others. For example, ice cream production companies across Europe may record bigger sales during the summer and lower sales during winter. Therefore, a sample of such entities drawn during winter will estimate winter-specific parameters.<\/p>\n<p><em>Reading 10 LOS 10k:<\/em><\/p>\n<p><em>Describe the issues regarding selection of the appropriate sample size, data-mining bias, sample selection bias, survivorship bias, look-ahead bias, and time-period bias.<\/em><\/p>\n<div class=\"notes_inv\"><hr \/>\n<p><a href=\"https:\/\/analystprep.com\/cfa-level-1-exam\/quantitative-methods\/learning-sessions-curriculum\/\"><em>Quantitative Methods \u2013 Learning Sessions<\/em><\/a><\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Sampling considerations refer to the desirable characteristics that should always be taken into account when selecting a sample so as to increase chances of accurate estimation of population parameters. In general, larger samples are preferred to smaller ones because the&#8230;<\/p>\n","protected":false},"author":2,"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-1596","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>Sampling Considerations &amp; Biases | CFA Level 1 - AnalystPrep<\/title>\n<meta name=\"description\" content=\"Learn key sampling considerations and common biases like time-period bias, data mining bias, and selection bias in statistical analysis.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/analystprep.com\/cfa-level-1-exam\/quantitative-methods\/sampling-considerations-common-biases\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sampling Considerations &amp; Biases | CFA Level 1 - 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