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2. Six Statistical Senses

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   Craiu, Radu V.; Gong, Ruobin; Meng, Xiao-Li (March 2023, Annual Review of Statistics and Its Application)

   This article proposes a set of categories, each one representing a particular distillation of important statistical ideas. Each category is labeled a “sense” because we think of these as essential in helping every statistical mind connect in constructive and insightful ways with statistical theory, methodologies, and computation, toward the ultimate goal of building statistical phronesis. The illustration of each sense with statistical principles and methods provides a sensical tour of the conceptual landscape of statistics, as a leading discipline in the data science ecosystem. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 10 is March 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

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5. Statistical Significance: Reliability of P-Values Compared to Other Statistical Summaries

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   Zawada, Jacqueline; Kolassa, John; Seifu, Yodit (December 2019, Current trends on biostatistics biometrics)

   Statistical inference has strongly relied on the use of p-values to draw conclusions. For over a decade this reliance on the p-value has been questioned by researches and academics. The question of whether p-values are truly the best standard, and what other possible statistics could replace p-values l has been discussed deeply. We set out to understand the amount of variation within p-values, and to find if they really are as reliable as the frequency of their use would suggest. To answer this question, we studied a set of clinical trials over the past two years. We also aim to describe the variety of information included in drag labels, and determine whether this information conforms to FDA guidelines. We found a large variation in the presentation of clinical trial data, much of which was not in line with the guidelines of the FDA. Our findings also show that among the clinical trials we studied there is more variation among the p-values than among the estimates. From this, we can conclude that the estimates from clinical trials should hold a heavy weight in the decision of whether or not to approve the drug. This finding suggests that there is validity to the skepticism of the reliance on p-values, and that further studies need to be done to find a new, more reliable, standard in statistical inference. 

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