![]() ![]() ![]() I remember early in my career an older researcher made an auspicious comment to me: "I find it quite interesting, and even a bit humorous, that you naively think that researchers are inherently immune to bias." However, people involved with research, science, and engineering know that is not necessarily the case. ![]() 1 We suspend our observational clinical judgement or healthy skepticism of the discussion and assume the researcher's quantitative data is unbiased and untainted. When we see numbers and quantitative analysis, by nature we surrender to the power of quantitative data, referred to as an overconfidence/overprecision bias or, informally, number bias. ![]() But have you ever gotten the feeling that you do not know how to judge if a presentation or paper you are seeing or reading is accurate? You may be reluctant to question the conclusions because you are not familiar with what a p-value is exactly, let alone the difference between ANOVA and MANOVA, correlation versus regression, or the statistical instrument that was used. Using the Statistical Fog to Tell a Compelling StoryĬertainly, statistics promise to extend our understanding beyond our individual knowledge and experience. ![]()
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