ArticleMarshall journal of medicine2018
Effect of removing outliers on statistical inference: implications to interpretation of experimental data in medical research.
Article in Marshall journal of medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
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3 authors.
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Abstract
backgroundData editing with elimination of "outliers" is commonly performed in the biomedical sciences. The effects of this type of data editing could influence study results, and with the vast and expanding amount of research in medicine, these effects would be magnified. METHODS AND
resultsWe first performed an anonymous survey of medical school faculty at institutions across the United States and found that indeed some form of outlier exclusion was performed by a large percentage of the respondents to the survey. We next performed Monte Carlo simulations of excluding high and low values from samplings from the same normal distribution. We found that removal of one pair of "outliers", specifically removal of the high and low values of the two samplings, respectively, had measurable effects on the type I error as the sample size was increased into the thousands. We developed an adjustment to the t score that accounts for the anticipated alteration of the type I error (t
conclusionData editing with elimination of outliers that includes removal of high and low values from two samples, respectively, can have significant effects on the occurrence of type 1 error. This type of data editing could have profound effects in high volume research fields, particularly in medicine, and we recommend an adjustment to the t score be used to reduce the potential for error.
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