PT - JOURNAL ARTICLE
AU - Ho, Joses
AU - Tumkaya, Tayfun
AU - Aryal, Sameer
AU - Choi, Hyungwon
AU - Claridge-Chang, Adam
TI - Moving beyond P values: Everyday data analysis with estimation plots
AID - 10.1101/377978
DP - 2018 Jan 01
TA - bioRxiv
PG - 377978
4099 - http://biorxiv.org/content/early/2018/07/26/377978.short
4100 - http://biorxiv.org/content/early/2018/07/26/377978.full
AB - Over the past 75 years, a number of statisticians have advised that the data-analysis method known as null-hypothesis significance testing (NHST) should be deprecated (Berkson, 1942; Halsey et al., 2015). The limitations of NHST have been extensively discussed, with an emerging consensus that current statistical practice in the biological sciences needs reform. However, there is less agreement on the specific nature of reform, with vigorous debate surrounding what would constitute a suitable alternative (Altman et al., 2000; Benjamin et al., 2017; Cumming and Calin-Jageman, 2016). An emerging view is that a more complete analytic technique would use statistical graphics to estimate effect sizes and their uncertainty (Cohen, 1994; Cumming and Calin-Jageman, 2016). As these estimation methods require only minimal statistical retraining, they have great potential to change the current data-analysis culture away from dichotomous thinking towards quantitative reasoning (Claridge-Chang and Assam, 2016). The evolution of statistics has been inextricably linked to the development of improved quantitative displays that support complex visual reasoning (Tufte, 2001). We consider that the graphic we describe here as an estimation plot is the most intuitive way to display the complete statistical information about experimental data sets. However, a major obstacle to adopting estimation is accessibility to suitable software. To overcome this hurdle, we have developed free software that makes high-quality estimation plotting available to all. Here, we explain the rationale for estimation plots by contrasting them with conventional charts used to display NHST data, and describe how the use of these graphs affords five major analytical advantages.