![]() ![]() ![]() This does not necessarily happen due to unethical behavior,īut rather to statistical ignorance or wishful thinking. ![]() In experimental disciplines, an experiment might be repeated more than once, and only the one that results in a small p-value are reported. In epidemiology in the social sciences for example, researchers may look for associations between an average outcome and several exposures, and report only the one exposure that resulted in a small p-value.įurthermore, they might try fitting several different models to adjust for confounding and pick the one model that yields the smallest p-value. ![]() P-hacking is a topic of much discussion because it is a problem in scientific publications.īecause publishers tend to reward statistically significant results over negative results, there’s an incentive to report significant results. This particular form of data dredging is referred to as p-hacking. Sim_data %>% filter(group = res$group) %>% In finding high correlations among variables that are theoretically uncorrelated. These examples are generally called data dredging, or data phishing, or data snooping or cherry picking.Īn example of data dredging would be if you look through many results produced by a random process, and pick the one that shows a relationship that supports the theory you want to defend.Ī Monte Carlo simulation can be used to show how data dredging can result Of course, the answer to both these questions is no. Or do divorces cause people to eat more margarine? Does this mean that margarine causes divorces, The example shows a very strong correlation between divorce rates and margarine consumption. The following comical example underscores that correlation is not causation. There are many reasons that a variable x can correlate with a variable y, without either being a cause for the other. Explain and give examples of Simpson’s Paradox.Ĭorrelation is Not Causation: Spurious Correlation.Understand how confounders can lead to the misinterpretation of associations.Explain how reversing cause and effect can lead to associations being confused with causation.Explain how outliers can drive correlation and learn to adjust for outliers using Spearman correlation.Identify examples of spurious correlation and explain how data dredging can lead to spurious correlation.After completing this section, you will be able to: ![]()
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