arXiv subjects

Jennifer L. Tackett

Publications and source records attributed to Jennifer L. Tackett.

3 recordsLinked to original sources

arXiv2018

Abandon Statistical Significance

We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm--and the p-value thresholds intrinsic to it--as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with currently subordinate factors (e.g., related prior evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain) as just one among many pieces of evidence. We have no desire to "ban" p-values or other purely statistical measures. Rather, we believe that such measures should not be thresholded and that, thresholded or not, they should not take priority over the currently subordinate factors. We also argue that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures. We offer recommendations for how our proposal can be implemented in the scientific publication process as well as in statistical decision making more broadly.

stat.ME↗

arXiv2018

Large Scale Replication Projects in Contemporary Psychological Research

Replication is complicated in psychological research because studies of a given psychological phenomenon can never be direct or exact replications of one another, and thus effect sizes vary from one study of the phenomenon to the next--an issue of clear importance for replication. Current large scale replication projects represent an important step forward for assessing replicability, but provide only limited information because they have thus far been designed in a manner such that heterogeneity either cannot be assessed or is intended to be eliminated. Consequently, the non-trivial degree of heterogeneity found in these projects represents a lower bound on heterogeneity. We recommend enriching large scale replication projects going forward by em- bracing heterogeneity. We argue this is key for assessing replicability: if effect sizes are sufficiently heterogeneous--even if the sign of the effect is consistent--the phenomenon in question does not seem particularly replicable and the theory underlying it seems poorly constructed and in need of enrichment. Uncovering why and revising theory in light of it will lead to improved theory that explains heterogeneity and in- creases replicability. Given this, large scale replication projects can play an important role not only in assessing replicability but also in advancing theory.

stat.ME↗

arXiv2018

Conceptualizing and Evaluating Replication Across Domains of Behavioral Research

We discuss the authors' conceptualization of replication, in particular the false dichotomy of direct versus conceptual replication intrinsic to it, and suggest a broader one that better generalizes to other domains of psychological research. We also discuss their approach to the evaluation of replication results and suggest moving beyond their dichotomous statistical paradigms and employing hierarchical / meta-analytic statistical models.

stat.AP↗