Beyond statistical significance: Five principles for the new era of data analysis and reporting

Abstract

A crisis of confidence in research findings in consumer psychology and other academic disciplines has led to various proposals to abandon, replace, strengthen, or supplement the null hypothesis significance testing paradigm. The proliferation of such proposals, and their often-conflicting recommendations, can increase confusion among researchers. We aim to bring some clarity by proposing five simple principles for the new era of data analysis and reporting of research in consumer psychology. We avoid adding to researchers' confusion and proposing more onerous or rigid standards. Our goal is to offer straightforward practical principles that are easy for researchers to keep in mind while analyzing their data and reporting their findings. These principles involve (1) interpreting p-values as continuous measures of the strength of evidence, (2) being aware of assumptions that determine whether one can rely on p-values, (3) using theory to establish the applicability of findings to new settings, (4) employing multiple measures of evidence and various processes to obtain them, but assigning special privilege to none, and (5) reporting procedures and findings transparently and completely. We hope that these principles provide researchers with some guidance and help to strengthen the reliability of the conclusions derived from their data, analyses, and findings.