{"id":16449,"date":"2017-11-03T21:25:13","date_gmt":"2017-11-03T15:55:13","guid":{"rendered":"http:\/\/www.fusioncharts.com\/blog\/?p=16449"},"modified":"2026-01-20T14:36:19","modified_gmt":"2026-01-20T09:06:19","slug":"common-biases-data-analysis","status":"publish","type":"post","link":"https:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/","title":{"rendered":"How to Debug Your Approach to Data Analysis in 2026"},"content":{"rendered":"<blockquote>7 common biases that influence how we understand, use, and interpret the world around us<\/blockquote>\r\nIn 2005, UCLA Econ Graduate, Michael Burry, saw the writing on the wall \u2013 the ticking numbers that form the American mortgage market. Burry\u2019s analysis of US lending practices between 2003-2004 led him to believe that housing prices would fall drastically as early as 2007.\r\n\r\nAnd he turned his ideas to good use, pocketing net profits close to a whopping <a href=\"https:\/\/www.vanityfair.com\/news\/2010\/04\/wall-street-excerpt-201004\" target=\"_blank\" rel=\"noopener\">489%<\/a> between 2001 and 2008! Those who overlooked his insights earned a little over 2% in the same period.\r\n\r\nIn the modern world, we can\u2019t overstate the impact of accurate data analysis. The price to pay for small mistakes can be significant \u2013 running up to millions of dollars, or the failure to predict election results by a laughably wide margin.\r\n\r\nSo, why do we make these errors? Why do even the best of us, with years of experience in making data-led decisions and equipped with the latest tools, often struggle to read between the numbers?\r\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_71 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\"><p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<\/div><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:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/#1_Approaching_data_sets_with_a_pre-existing_idea\" title=\"1. Approaching data sets with a pre-existing idea\">1. Approaching data sets with a pre-existing idea<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/#2_Not_looking_at_the_data_ALL_the_data_and_nothing_but_the_data\" title=\"2. Not looking at the data, ALL the data, and nothing but the data\">2. Not looking at the data, ALL the data, and nothing but the data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/#3_Ignoring_the_impact_of_outliers_or_rejecting_them_altogether\" title=\"3. Ignoring the impact of outliers (or rejecting them altogether)\">3. Ignoring the impact of outliers (or rejecting them altogether)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/#4_Assuming_a_trend_recurring_in_two_data_sets_holds_true_for_the_combined_set_as_well\" title=\"4. Assuming a trend recurring in two data sets, holds true for the combined set as well\">4. Assuming a trend recurring in two data sets, holds true for the combined set as well<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/#5_Accepting_a_single_layer_of_analysis_if_it_doesnt_display_any_contradictions\" title=\"5. Accepting a single layer of analysis if it doesn\u2019t display any contradictions\">5. Accepting a single layer of analysis if it doesn\u2019t display any contradictions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/#6_Putting_a_square_peg_in_a_round_hole\" title=\"6. Putting a square peg in a round hole\">6. Putting a square peg in a round hole<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/#7_Expecting_the_usual-case_scenario\" title=\"7. Expecting the usual-case scenario\">7. Expecting the usual-case scenario<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.fusioncharts.com\/blog\/common-biases-data-analysis\/#Dont_Blame_the_Bias\" title=\"Don\u2019t Blame the Bias\">Don\u2019t Blame the Bias<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"1_Approaching_data_sets_with_a_pre-existing_idea\"><\/span>1. Approaching data sets with a pre-existing idea<span class=\"ez-toc-section-end\"><\/span><\/h2>\r\nAlso called confirmation bias, this theory suggests that decision makers use data to prove or debunk a specific theory.\r\n<a href=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/11\/the-confirmation-bias.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-16494\" src=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/11\/the-confirmation-bias.jpg\" alt=\"the confirmation bias image\" width=\"2100\" height=\"1400\" srcset=\"\/blog\/wp-content\/uploads\/2017\/11\/the-confirmation-bias.jpg 2100w, \/blog\/wp-content\/uploads\/2017\/11\/the-confirmation-bias-150x100.jpg 150w\" sizes=\"auto, (max-width: 2100px) 100vw, 2100px\" \/><\/a> <a href=\"https:\/\/jamesclear.com\/wp-content\/uploads\/2015\/09\/confirmation-bias.jpg?x32321\" target=\"_blank\" rel=\"nofollow noopener\">Image Source<\/a>\r\nUnlike Burry, most stakeholders looked at the data with preconceived notions of how the investment market is supposed to behave.\r\n\r\nInstead of a generic stance, C-level executives might leverage data with a predetermined goal. That\u2019s where the data scientist comes in \u2013 it\u2019s their job to perform an accurate and objective analysis, gaining insights that may or may not validate the business users\u2019 choice, or even turn out to be completely irrelevant.\r\n<h2><span class=\"ez-toc-section\" id=\"2_Not_looking_at_the_data_ALL_the_data_and_nothing_but_the_data\"><\/span>2. Not looking at the data, ALL the data, and nothing but the data<span class=\"ez-toc-section-end\"><\/span><\/h2>\r\nThe broad umbrella of selection bias covers unwitting biases (like survivorship bias) or unavoidable ones, such as availability bias.\r\n\r\nTake, for example, the 7 million Americans living outside the country who weren\u2019t included in 2016\u2019s US pre-poll survey. Incomplete data sets let the NYT Presidential Forecast ticker go from 80% to &lt;5% in around 12 hours.\r\n<a href=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/11\/elections-chance-of-winning.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-16493\" src=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/11\/elections-chance-of-winning.png\" alt=\"elections chance of winning\" width=\"2000\" height=\"1060\" srcset=\"\/blog\/wp-content\/uploads\/2017\/11\/elections-chance-of-winning.png 2000w, \/blog\/wp-content\/uploads\/2017\/11\/elections-chance-of-winning-150x80.png 150w\" sizes=\"auto, (max-width: 2000px) 100vw, 2000px\" \/><\/a>\r\nIn fact, most surveys are prey to selection bias. \u201cMany businesses only capture a small piece of the pie when it comes to data available to their segment or industry, and this means their data and subsequent analysis are skewed,\u201d said Powerlytics CEO, Kevin Sheets, in an interview with <a href=\"https:\/\/www.informationweek.com\/big-data\/big-data-analytics\/7-common-biases-that-skew-big-data-results\/d\/d-id\/1321211?image_number=6\" target=\"_blank\" rel=\"noopener\">InformationWeek<\/a>.\r\n<h2><span class=\"ez-toc-section\" id=\"3_Ignoring_the_impact_of_outliers_or_rejecting_them_altogether\"><\/span>3. Ignoring the impact of outliers (or rejecting them altogether)<span class=\"ez-toc-section-end\"><\/span><\/h2>\r\nOutliers are extreme data points that show a vast difference from the mean. As seen, they tend to generate \u2018false\u2019 averages that don\u2019t reflect the real picture.\r\n\r\nIn 2014, research shows the bottom 50% of the American population earned USD 25,000 on an average, while the top 1% cashed in around 81 times that amount, every year &#8211; sizable difference.\r\n<a href=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/10\/Scatter-Plot_1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-16460\" src=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/10\/Scatter-Plot_1.png\" alt=\"\" width=\"2859\" height=\"1476\" srcset=\"\/blog\/wp-content\/uploads\/2017\/10\/Scatter-Plot_1.png 2859w, \/blog\/wp-content\/uploads\/2017\/10\/Scatter-Plot_1-150x77.png 150w\" sizes=\"auto, (max-width: 2859px) 100vw, 2859px\" \/><\/a>\r\nHowever, removing the outliers isn\u2019t always the way forward. For the insurance industry, a set of exceptional claims can impact revenues \u2013 but must be analyzed and addressed separately.\r\n<h2><span class=\"ez-toc-section\" id=\"4_Assuming_a_trend_recurring_in_two_data_sets_holds_true_for_the_combined_set_as_well\"><\/span>4. Assuming a trend recurring in two data sets, holds true for the combined set as well<span class=\"ez-toc-section-end\"><\/span><\/h2>\r\nThose of us not familiar with the nitty gritty of statistical analysis often fall prey to what experts call \u2018Simpson\u2019s Paradox\u2019. It says, combining two data sets might negate \u2013 or even reverse \u2013 the insights gathered from them individually.\r\n\r\nLet\u2019s break it down: in 1973, graduate admissions in Berkeley showed a marked slant towards men who enjoyed 44% successful admission rates, in comparison to 35% for women. But among the 6 largest departments, 4 were biased against men while only 2 favored them!\r\n<a href=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/10\/Screen-Shot-2017-11-03-at-8.21.41-PM.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-16461\" src=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/10\/Screen-Shot-2017-11-03-at-8.21.41-PM.png\" alt=\"Simpson's Paradox \" width=\"1152\" height=\"432\" srcset=\"\/blog\/wp-content\/uploads\/2017\/10\/Screen-Shot-2017-11-03-at-8.21.41-PM.png 1152w, \/blog\/wp-content\/uploads\/2017\/10\/Screen-Shot-2017-11-03-at-8.21.41-PM-150x56.png 150w\" sizes=\"auto, (max-width: 1152px) 100vw, 1152px\" \/><\/a>\r\n\r\nInterestingly, the Simpson\u2019s Paradox disappears when you factor in causes and other underlying forces.\r\n\r\nIn our example, it was observed that women mostly applied to highly competitive departments \u2013 among the 341 who went for Department F, only 7% finally qualified. On the other hand, from the measly 25 who chose the less competitive B, 68% were successful.\r\n\r\nThis brings us to the cause \u2013 hidden data, called confounding variables, that can hugely impact your analyses.\r\n<h2><span class=\"ez-toc-section\" id=\"5_Accepting_a_single_layer_of_analysis_if_it_doesnt_display_any_contradictions\"><\/span>5. Accepting a single layer of analysis if it doesn\u2019t display any contradictions<span class=\"ez-toc-section-end\"><\/span><\/h2>\r\nAt first glance, an insight may appear to make perfect sense \u2013 and if accepted, can lead to incorrect decisions. Let\u2019s say a study of men and women uncovers that men gain weight faster and more easily than women, leading to the conclusion that gender is a direct cause.\r\n\r\nOn closer examination, however, it\u2019s revealed that the average man eats more than women, and is more likely to have a desk job.\r\n\r\nThis is a curious case of the confounding variable, where an earlier overlooked piece of data invalidates the conclusion. In the Berkeley scenario, the fact that women preferred highly competitive courses negated the apparent favoritism towards men.\r\n<a href=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/11\/kyle-glenn-336141-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-16497\" src=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/11\/kyle-glenn-336141-1.png\" alt=\"contradiction \" width=\"600\" height=\"401\" srcset=\"\/blog\/wp-content\/uploads\/2017\/11\/kyle-glenn-336141-1.png 600w, \/blog\/wp-content\/uploads\/2017\/11\/kyle-glenn-336141-1-150x100.png 150w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/a>\r\nClearly, the obvious conclusion isn\u2019t always the right one.\r\n<h2><span class=\"ez-toc-section\" id=\"6_Putting_a_square_peg_in_a_round_hole\"><\/span>6. Putting a square peg in a round hole<span class=\"ez-toc-section-end\"><\/span><\/h2>\r\nRight at the starting line, if the analytical model employed is out of sync with the data set, the insights generated might be subject to either overfitting or underfitting.\r\n<a href=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/10\/S_peg.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"906\" class=\"alignnone size-full wp-image-16459\" src=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/10\/S_peg.png\" alt=\"square peg in a round hole\" srcset=\"\/blog\/wp-content\/uploads\/2017\/10\/S_peg.png 1400w, \/blog\/wp-content\/uploads\/2017\/10\/S_peg-150x97.png 150w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><\/a>\r\n\r\nOverfitting arises from statistical models that are overly complex and thorough, taking into account more information than was required. Underfitting, on the other hand, is a result of applying models that are too simple. Not enough aspects are considered, and in both cases the conclusions are likely to be skewed.\r\n\r\nMathematician Spencer Greenberg sums it up perfectly: &#8220;<a href=\"https:\/\/www.informationweek.com\/big-data\/big-data-analytics\/7-common-biases-that-skew-big-data-results\/d\/d-id\/1321211?image_number=6\" target=\"_blank\" rel=\"noopener\">Overfitting<\/a> is one of the most common (and worrisome) biases. It comes about from checking lots of different hypotheses in data. If each hypothesis you check has, say, a 1 in 20 chance of being a false positive, then if you check 20 different hypotheses, you&#8217;re very likely to have a false positive occur at least once.\u201d\r\n<h2><span class=\"ez-toc-section\" id=\"7_Expecting_the_usual-case_scenario\"><\/span>7. Expecting the usual-case scenario<span class=\"ez-toc-section-end\"><\/span><\/h2>\r\nNormalcy bias occurs when we fail to factor in non-normality, i.e. atypical possibilities.\r\nSome statistical tests, like the t-test, is predicated on the fact that a bell curve \u2013 a normal distribution \u2013 already exists. However, if that\u2019s not actually the case and data is force-fit into compliance, the conclusions can be vastly misleading.\r\n\r\nFor instance, a hospital\u2019s target processing time for patients in the emergency room is 4 hours. However, on-floor data mapped as a bell curve suggests it hovers between 12 hours, and 30 minutes! Does that mean the systems in place are critically flawed?\r\n\r\nNot necessarily.\r\n\r\nGreenberg recalls how a t-test returned a probability value of 0.03, meaning the hypothesis being tested had a 0.03% chance of being true. When passed through non-parametric analysis that doesn&#8217;t assume that the data is normal, the same experiment gave a result of 0.06 \u2013 a small but visible change.\r\n<h2><span class=\"ez-toc-section\" id=\"Dont_Blame_the_Bias\"><\/span>Don\u2019t Blame the Bias<span class=\"ez-toc-section-end\"><\/span><\/h2>\r\nAnd the list doesn\u2019t end. From prediction bias to loss aversions, it\u2019s almost as if the human mind is built for flawed data analyses! Yet, biases are hardwired into our thought processes and a vital part of our organic survival mechanisms.\r\n\r\nThink about it. In case of a zombie apocalypse, is it better to a) contemplate the forces that would reanimate a corpse and instill it with the desire to eat human flesh, then work out the most effective solution to block this cycle? Or, b) start shooting until it stopped moving.\r\n\r\nThe difference is, while time and energy continue to be precious resources, modern computational tools and analytical methods have far surpassed such cognitive limitations. Errors can now be easily avoided by applying the right tools on the right information \u2013 all you have to do is deep-dive, and explore the vast, ever-growing world of analysis ideas. While you&#8217;re at it, why don&#8217;t you enjoy this comic strip:\r\n<a href=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/10\/Dilbert-Data-Analysis-Bias.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-16462\" src=\"https:\/\/www.fusioncharts.com\/blog\/wp-content\/uploads\/2017\/10\/Dilbert-Data-Analysis-Bias.gif\" alt=\"data analysis bias dilbert\" width=\"900\" height=\"281\" \/><\/a>","protected":false},"excerpt":{"rendered":"<p>7 common biases that influence how we understand, use, and interpret the world around us In 2005, UCLA Econ Graduate, Michael Burry, saw the writing on the wall \u2013 the ticking numbers that form the American mortgage market. Burry\u2019s analysis of US lending practices between 2003-2004 led him to believe that housing prices would fall [&hellip;]<\/p>\n","protected":false},"author":37,"featured_media":16467,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[],"coauthors":[678],"class_list":["post-16449","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thoughts"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How To Debug Your Approach To Data Analysis<\/title>\n<meta name=\"description\" content=\"Avoid common pitfalls like confirmation bias. 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