{"id":173787,"date":"2023-11-26T12:14:28","date_gmt":"2023-11-26T11:14:28","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=173787"},"modified":"2023-11-26T12:17:55","modified_gmt":"2023-11-26T11:17:55","slug":"4-types-of-statistical-bias-to-avoid-in-your-analyses","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses","title":{"rendered":"4 types of statistical bias to avoid in your analyses"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"173787\" class=\"elementor elementor-173787\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-dd7583b e-flex e-con-boxed e-con\" data-id=\"dd7583b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a78c5d7 elementor-widget elementor-widget-heading\" data-id=\"a78c5d7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Statistical bias can be defined as anything that leads to a systematic difference between the true parameters of a population and the statistics used to estimate these parameters. There is a long list of types of statistical bias.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a1c7429 elementor-widget elementor-widget-text-editor\" data-id=\"a1c7429\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>We&#8217;ve decided to present these four types to you, because they&#8217;re the ones we see most affecting the daily lives of <a href=\"https:\/\/datascientest.com\/en\/essential-data-scientist-tools-for-your-daily-workflow\">data scientists<\/a> and analysts.<\/p><p>We&#8217;ll describe them and give you a concrete example.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f4c4d15 e-flex e-con-boxed e-con\" data-id=\"f4c4d15\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8bec503 elementor-widget elementor-widget-heading\" data-id=\"8bec503\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">1) Selection bias<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c850d94 elementor-widget elementor-widget-text-editor\" data-id=\"c850d94\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Selection bias occurs when, as the name suggests, the <strong>data selection<\/strong> is skewed. This generally means that you are working with a specific subset of your group, rather than a random subset.<\/p><p>For example, if you want to launch a new product, before spending time and money, you need to know if your audience is interested in it.<\/p><p>So, you conduct a survey that you send to your existing customers. While this is certainly an important part of your audience, it does not represent the <strong>entirety of your audience.<\/strong> You have just introduced a selection bias that can cost you dearly!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-94a7ada elementor-widget elementor-widget-image\" data-id=\"94a7ada\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/biais-statistique.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-072749d e-flex e-con-boxed e-con\" data-id=\"072749d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-fd916d5 elementor-widget elementor-widget-heading\" data-id=\"fd916d5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2) Recall bias\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1538596 elementor-widget elementor-widget-text-editor\" data-id=\"1538596\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Recall bias<\/strong> is another common error in survey situations, especially in feedback scenarios. It occurs when participants cannot remember past events, memories, or details accurately.<\/p><p><strong>Human memory<\/strong> is inherently selective, which is a normal phenomenon but can make research more challenging.<\/p><p>The human brain tends to recall good memories more readily than bad ones. For instance, if you conduct a survey after a conference, it&#8217;s preferable to send the questionnaire promptly if you want it to be more accurate.<\/p><p>This is because memories can fade or become distorted over time, and people are more likely to remember the positive aspects of an event while forgetting or downplaying the negative ones.<\/p><p>Therefore, collecting feedback as soon as possible after an event can help reduce recall bias and provide more accurate responses.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-93e4f8f e-flex e-con-boxed e-con\" data-id=\"93e4f8f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-04566f9 elementor-widget elementor-widget-heading\" data-id=\"04566f9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3)Recall bias<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d12cff1 elementor-widget elementor-widget-text-editor\" data-id=\"d12cff1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Recall bias<\/strong> is another common error in survey situations, especially in feedback scenarios. It occurs when participants cannot remember past events, memories, or details accurately.<\/p><p>Human memory is inherently selective, which is a<strong> normal phenomenon but can make research more challenging<\/strong>.<\/p><p>The human brain tends to recall good memories more readily than bad ones. For instance, if you conduct a survey after a conference, it&#8217;s preferable to send the questionnaire promptly if you want it to be more accurate.<\/p><p>This is because memories can fade or become distorted over time, and people are more likely to remember the positive aspects of an event while forgetting or downplaying the negative ones.<\/p><p>Therefore, collecting feedback as soon as possible after an event can help reduce recall bias and provide more accurate responses.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e23c5d elementor-widget elementor-widget-image\" data-id=\"3e23c5d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/biais-des-survivants.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-299a8ae e-flex e-con-boxed e-con\" data-id=\"299a8ae\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-46f67ef elementor-widget elementor-widget-heading\" data-id=\"46f67ef\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">4) Omitted variable bias<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-19c2604 elementor-widget elementor-widget-text-editor\" data-id=\"19c2604\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Indeed, this is a bias known as &#8220;omission bias&#8221; in statistics and modeling. This bias occurs when relevant variables are not included in a model, which can lead to incorrect predictions or conclusions. In the context of <a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\">Machine Learning,<\/a> it means that if you do not consider important features for a model, it can become ineffective or provide inaccurate results.<\/p><p>Your concrete example illustrates this concept well. When buying a car, if you only consider the price without taking into account other factors such as mileage, year of manufacture, overall condition, etc., you could make a choice based on an omission bias.<\/p><p>In this example, omitting the high mileage has a significant impact on the actual value of the car.<\/p><p>It is essential to carefully choose the variables to include in a <a href=\"https:\/\/datascientest.com\/en\/boosting-business-with-3-essential-machine-learning-algorithms\">Machine Learning model<\/a> to ensure that it adequately captures relevant information and produces reliable results.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ae69d21 e-flex e-con-boxed e-con\" data-id=\"ae69d21\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a639dc8 elementor-widget elementor-widget-text-editor\" data-id=\"a639dc8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We have provided you with a <strong>non-exhaustive list<\/strong> of the main statistical biases that are commonly <a href=\"https:\/\/datascientest.com\/en\/data-science-in-education-how-data-is-transforming-schools\">encountered in Data Science and in our daily lives.<\/a><\/p><p>It is important to emphasize that biased statistics are bad statistics. It is always essential to strive to minimize biases as much as possible. One very effective technique to avoid biases is randomization, for instance. Ensuring that the study sample is drawn randomly can help mitigate biases.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-8bbe302 e-flex e-con-boxed e-con\" data-id=\"8bbe302\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4835ab7 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"4835ab7\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our Data Science courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Statistical bias can be defined as anything that leads to a systematic difference between the true parameters of a population and the statistics used to estimate these parameters. There is a long list of types of statistical bias. We&#8217;ve decided to present these four types to you, because they&#8217;re the ones we see most affecting [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":173798,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>4 types of statistical bias to avoid in your analyses<\/title>\n<meta name=\"description\" content=\"There are four types of statistical bias that you should strive to avoid in your analyses: Selection Bias: This occurs when the sample used for analysis is not representative of the larger population you are trying to draw\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"4 types of statistical bias to avoid in your analyses\" \/>\n<meta property=\"og:description\" content=\"Statistical bias can be defined as anything that leads to a systematic difference between the true parameters of a population and the statistics used to\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses\" \/>\n<meta property=\"og:site_name\" content=\"Data Science Courses | DataScientest\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2023-11-26T11:14:28+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-26T11:17:55+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/statistik-bias.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"562\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Melanie\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:site\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Melanie\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses#article\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses\"},\"author\":{\"name\":\"Melanie\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d\"},\"headline\":\"4 types of statistical bias to avoid in your analyses\",\"datePublished\":\"2023-11-26T11:14:28+00:00\",\"dateModified\":\"2023-11-26T11:17:55+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses\"},\"wordCount\":675,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"articleSection\":[\"Data Science\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses#respond\"]}],\"copyrightYear\":\"2023\",\"copyrightHolder\":{\"@id\":\"https:\/\/datascientest.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses\",\"url\":\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses\",\"name\":\"4 types of statistical bias to avoid in your analyses\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/#website\"},\"datePublished\":\"2023-11-26T11:14:28+00:00\",\"dateModified\":\"2023-11-26T11:17:55+00:00\",\"description\":\"There are four types of statistical bias that you should strive to avoid in your analyses: Selection Bias: This occurs when the sample used for analysis is not representative of the larger population you are trying to draw\",\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/en\/#website\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"name\":\"Data Science Courses | DataScientest\",\"description\":\"Leader in Data Science Courses\",\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/en\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/datascientest.com\/en\/#organization\",\"name\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"contentUrl\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"width\":301,\"height\":301,\"caption\":\"DataScientest\"},\"image\":{\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/DataScientestEN\",\"https:\/\/twitter.com\/DataScientest__\",\"https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/\",\"https:\/\/www.tiktok.com\/@datascientest\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d\",\"name\":\"Melanie\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g\",\"caption\":\"Melanie\"},\"url\":\"https:\/\/datascientest.com\/en\/author\/melaniekrause\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"4 types of statistical bias to avoid in your analyses","description":"There are four types of statistical bias that you should strive to avoid in your analyses: Selection Bias: This occurs when the sample used for analysis is not representative of the larger population you are trying to draw","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses","og_locale":"en_GB","og_type":"article","og_title":"4 types of statistical bias to avoid in your analyses","og_description":"Statistical bias can be defined as anything that leads to a systematic difference between the true parameters of a population and the statistics used to","og_url":"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses","og_site_name":"Data Science Courses | DataScientest","article_publisher":"https:\/\/www.facebook.com\/DataScientestEN","article_published_time":"2023-11-26T11:14:28+00:00","article_modified_time":"2023-11-26T11:17:55+00:00","og_image":[{"width":1024,"height":562,"url":"https:\/\/datascientest.com\/en\/files\/2023\/11\/statistik-bias.webp","type":"image\/webp"}],"author":"Melanie","twitter_card":"summary_large_image","twitter_creator":"@DataScientest__","twitter_site":"@DataScientest__","twitter_misc":{"Written by":"Melanie","Estimated reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses#article","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses"},"author":{"name":"Melanie","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d"},"headline":"4 types of statistical bias to avoid in your analyses","datePublished":"2023-11-26T11:14:28+00:00","dateModified":"2023-11-26T11:17:55+00:00","mainEntityOfPage":{"@id":"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses"},"wordCount":675,"commentCount":0,"publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"articleSection":["Data Science"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses#respond"]}],"copyrightYear":"2023","copyrightHolder":{"@id":"https:\/\/datascientest.com\/#organization"}},{"@type":"WebPage","@id":"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses","url":"https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses","name":"4 types of statistical bias to avoid in your analyses","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/#website"},"datePublished":"2023-11-26T11:14:28+00:00","dateModified":"2023-11-26T11:17:55+00:00","description":"There are four types of statistical bias that you should strive to avoid in your analyses: Selection Bias: This occurs when the sample used for analysis is not representative of the larger population you are trying to draw","inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/en\/4-types-of-statistical-bias-to-avoid-in-your-analyses"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/en\/#website","url":"https:\/\/datascientest.com\/en\/","name":"Data Science Courses | DataScientest","description":"Leader in Data Science Courses","publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/en\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-GB"},{"@type":"Organization","@id":"https:\/\/datascientest.com\/en\/#organization","name":"DataScientest","url":"https:\/\/datascientest.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","contentUrl":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","width":301,"height":301,"caption":"DataScientest"},"image":{"@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataScientestEN","https:\/\/twitter.com\/DataScientest__","https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/","https:\/\/www.tiktok.com\/@datascientest"]},{"@type":"Person","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d","name":"Melanie","image":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g","caption":"Melanie"},"url":"https:\/\/datascientest.com\/en\/author\/melaniekrause"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/173787"}],"collection":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/users\/67"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/comments?post=173787"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/173787\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media\/173798"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media?parent=173787"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/categories?post=173787"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/tags?post=173787"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}