{"id":1073,"date":"2024-04-14T20:38:01","date_gmt":"2024-04-14T18:38:01","guid":{"rendered":"https:\/\/guillaume-guerard.com\/?page_id=1073"},"modified":"2024-04-14T20:46:38","modified_gmt":"2024-04-14T18:46:38","slug":"biais-dechantillonnage","status":"publish","type":"page","link":"https:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/biais-dechantillonnage\/","title":{"rendered":"Sampling bias"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"1073\" class=\"elementor elementor-1073\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b44831c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b44831c\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element 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elementor-top-column elementor-element elementor-element-2971854\" data-id=\"2971854\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-00e0c70 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"00e0c70\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Data 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data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Page 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 Contents\"><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:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/biais-dechantillonnage\/#Les_biais_dechantillonnage\" >Sampling bias<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/biais-dechantillonnage\/#Causes\" >Causes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/biais-dechantillonnage\/#Types_de_biais_dechantillonnage\" >Types of Sampling Bias<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/biais-dechantillonnage\/#Comment_eviter_les_biais_dechantillonnage\" >How to avoid sampling bias<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/biais-dechantillonnage\/#Exemple_de_correction\" >Example of correction<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Les_biais_dechantillonnage\"><\/span>Sampling bias<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cbcdfb2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cbcdfb2\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c6cc748\" data-id=\"c6cc748\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0b7f565 elementor-widget elementor-widget-text-editor\" data-id=\"0b7f565\" data-element_type=\"widget\" data-e-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\t\t<p>Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. We also talk about verification bias in medical fields. Sampling bias limits the generalizability of the <a href=\"https:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/parfaire-la-section-resultats\/\">results<\/a> because it poses a threat to external validity, particularly population validity. In other words, results from biased samples can only be generalized to populations that share characteristics with the sample.<\/p><p><img decoding=\"async\" class=\"alignnone wp-image-49 size-thumbnail\" src=\"http:\/\/guillaume-guerard.com\/wp-content\/uploads\/2023\/07\/logo_sc-150x150.png\" alt=\"biais d'\u00e9chantillonnage\" width=\"150\" height=\"150\" srcset=\"https:\/\/guillaume-guerard.com\/wp-content\/uploads\/2023\/07\/logo_sc-150x150.png 150w, https:\/\/guillaume-guerard.com\/wp-content\/uploads\/2023\/07\/logo_sc-70x70.png 70w, https:\/\/guillaume-guerard.com\/wp-content\/uploads\/2023\/07\/logo_sc-125x125.png 125w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/p>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-eb2fbc6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eb2fbc6\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-033ddc3\" data-id=\"033ddc3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-01a9c83 elementor-widget elementor-widget-heading\" data-id=\"01a9c83\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Causes\"><\/span>Causes<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e9cee66 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e9cee66\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b6d0a10\" data-id=\"b6d0a10\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1595530 elementor-widget elementor-widget-text-editor\" data-id=\"1595530\" data-element_type=\"widget\" data-e-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\t\t<p>Your choice of design <a href=\"https:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/\">research<\/a> or <a href=\"https:\/\/guillaume-guerard.com\/en\/cours-de-methodologie\/parfaire-la-methodologie\/\">method<\/a> data collection may result in sampling bias. This type of research bias can occur in both probability and non-probability sampling.<\/p><p>In probability sampling, each member of the population has a known chance of being selected. For example, you can use a random number generator to select a simple random sample from your population.<\/p><p>Although this procedure reduces the risk of sampling bias, it cannot eliminate it. If your sampling frame \u2013 the actual list of individuals from which the sample is drawn \u2013 does not match the population, this can result in a biased sample.<\/p><p>You want to study the levels of procrastination and social anxiety among undergraduate students at your university using a simple random sample. You assign a number to each student in the research participant database, from 1 to 1,500, and use a random number generator to select 120 numbers.<\/p><p>Even though you used a random sample, not everyone in your target population \u2013 undergraduates at your university \u2013 had a chance of being selected. Your sample does not contain everyone who did not sign up to be contacted about their research participation. This may bias your sample toward people who have less social anxiety and are more willing to participate in research.<\/p><p>A non-probability sample is selected based on non-random criteria. For example, in a convenience sample, participants are selected based on their accessibility and availability.<\/p><p>Non-probability sampling often results in biased samples because some members of the population are more likely to be included than others.<\/p><p>You want to study the popularity of plant-based foods among undergraduate students at your university. For convenience, you send a survey to everyone enrolled in introductory psychology courses at your university. They all complete it in exchange for course credits.<\/p><p>Since this is a convenience sample, it is not representative of your target population. People taking this course may be more liberal and attracted to plant-based foods than other students at your university.<\/p>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-daff584 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"daff584\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d35671c\" data-id=\"d35671c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6daa903 elementor-widget elementor-widget-heading\" data-id=\"6daa903\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Types_de_biais_dechantillonnage\"><\/span>Types of Sampling Bias<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ffb2d92 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ffb2d92\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e8acd42\" data-id=\"e8acd42\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-66b977e elementor-widget elementor-widget-text-editor\" data-id=\"66b977e\" data-element_type=\"widget\" data-e-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\t\t<p>Self-selection bias<\/p><ul><li>People with specific characteristics are more likely than others to agree to participate in a study.<\/li><li>People who seek more thrills are likely to participate in pain research studies. This can skew the data.<\/li><\/ul><p>Non-response bias<\/p><ul><li>People who refuse to participate or drop out of a study differ systematically from those who participate.<\/li><li>In a study on stress and workload, employees with high workload were less likely to participate. The resulting sample may not vary significantly in terms of workload.<\/li><\/ul><p>Undercount bias<\/p><ul><li>Some members of a population are insufficiently represented in the sample.<\/li><li>Administering general national surveys online risks overlooking groups with limited Internet access, such as older adults and low-income households.<\/li><\/ul><p>Survivorship bias<\/p><ul><li>Successful observations, people and objects are more likely to be represented in the sample than unsuccessful observations.<\/li><li>In scientific journals, there is a strong publication bias towards positive results. Successful research results are published much more often than unsuccessful results.<\/li><\/ul><p>Pre-selection or advertising bias<\/p><ul><li>How participants are pre-screened or where a study is advertised can bias a sample.<\/li><li>When looking for volunteers to test a new sleep intervention, you may end up with a sample that is more motivated to improve their sleep habits than the rest of the population. As a result, they could have improved their sleep habits regardless of the effects of your intervention.<\/li><\/ul><p>Healthy User Bias<\/p><ul><li>Volunteers for preventive interventions are more likely to engage in health-promoting behaviors and activities than other members of the population.<\/li><li>A sample participating in a preventive intervention has a better diet, higher levels of physical activity, abstains from alcohol, and avoids smoking more than most of the population. Experimental results may result from the interaction of the treatment with these sample characteristics, rather than just the treatment itself.<\/li><\/ul>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9958605 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9958605\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cad1990\" data-id=\"cad1990\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5b8ffaa elementor-widget elementor-widget-heading\" data-id=\"5b8ffaa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Comment_eviter_les_biais_dechantillonnage\"><\/span>How to avoid sampling bias<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-db8ec75 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"db8ec75\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b9e70c9\" data-id=\"b9e70c9\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b5bd580 elementor-widget elementor-widget-text-editor\" data-id=\"b5bd580\" data-element_type=\"widget\" data-e-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\t\t<p>Using careful research design and sampling procedures can help you avoid sampling bias.<\/p><p>Define a target population and a sampling frame (the list of individuals from which the sample will be drawn). Match the sampling frame to the target population as much as possible to reduce the risk of sampling bias.<\/p><p>Make online surveys as short and accessible as possible.<\/p><p>Follow up on non-respondents.<\/p><p>Avoid convenience sampling.<\/p><p>Oversampling can be used to avoid sampling bias in situations where members of defined groups are underrepresented (undercount). This is a method of selecting respondents from certain groups so that they constitute a larger part of the sample than that of the population.<\/p><p>Once all data is collected, responses from oversampled groups are weighted relative to their actual share of the population to eliminate any sampling bias.<\/p>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-45122c6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"45122c6\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1742512\" data-id=\"1742512\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3e8b2be elementor-widget elementor-widget-heading\" data-id=\"3e8b2be\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Exemple_de_correction\"><\/span>Example of correction<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-61181cb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"61181cb\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d8c3297\" data-id=\"d8c3297\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-de3477c elementor-widget elementor-widget-text-editor\" data-id=\"de3477c\" data-element_type=\"widget\" data-e-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\t\t<p>A researcher wants to study the political views of different ethnic groups in the United States and focus in depth on Asian Americans, who only make up 5.6 % of the U.S. population. The researcher wants to study each ethnic group separately, but also gather enough data on Asian Americans to be able to draw accurate conclusions.<\/p><p>They gather a nationally representative sample, with 1,500 respondents, that oversamples Asian Americans. Random dialing is used to contact U.S. households, and disproportionately larger samples are taken from areas with more Asian Americans. Of the 1,500 people surveyed, 336 were Asian Americans. Based on this sample size, the researcher can be confident in their findings about Asian Americans.<\/p><p>Weighting is applied to ensure that Asian American responses represent 5.6 % of the total. This allows for accurate estimates of the sample as a whole.<\/p>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Home Data Science Sampling Bias Sampling bias occurs when certain members of a population are systematically more likely\u2026 <\/p>","protected":false},"author":1,"featured_media":0,"parent":47,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":""},"class_list":["post-1073","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/pages\/1073","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/comments?post=1073"}],"version-history":[{"count":7,"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/pages\/1073\/revisions"}],"predecessor-version":[{"id":1085,"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/pages\/1073\/revisions\/1085"}],"up":[{"embeddable":true,"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/pages\/47"}],"wp:attachment":[{"href":"https:\/\/guillaume-guerard.com\/en\/wp-json\/wp\/v2\/media?parent=1073"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}