ScienceGeist

When evaluating the strength of the evidence, we should consider auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold is not acceptable.

P-values and significance testing have come under increasing scrutiny in scientific research. How accurate are these methods for indicating whether a hypothesis is valid?

John Ioannidis discusses the potential effects on clinical research of a 2017 proposal to lower the default P value threshold for statistical significance from .05 to .005 as a means to reduce false-positive findings.

A study has revealed a high prevalence of inconsistencies in reported statistical test results. Such inconsistencies make results unreliable, as they become “irreproducible”, and ultimately affect the level of trust in scientific reporting.

Statistical significance and hypothesis testing are not really helpful when it comes to testing our hypotheses.

Reproducible research includes sharing data and code.  The reproducibility policy at the journal Biostatistics rewards articles with badges for data and code sharing.  This study investigates the effect of badges at increasing reproducible research, specifically, data and code sharing, at Biostatistics.

A new paper recommends that the label “statistically significant” be dropped altogether; instead, researchers should describe and justify their decisions about study design and interpretation of the data, including the statistical threshold.

Time series and international data are also included.

When statistical fudging is buried in the way data are sliced and diced after the fact or put through tortured analysis in a search for significant results.

We wish to answer this question: If you observe a ‘significant’ p -value after doing a single unbiased experiment, what is the probability that your result is a false positive?

According to its developers, Statcheck gets it right in more than 95% of cases. Some outsiders still aren’t convinced.

When Dutch researchers developed an open-source algorithm designed to flag statistical errors in psychology papers, it received mixed reaction from the research community.

Comments on "Redefine statistical significance"

Researchers who want professorships are sometimes driven to publish suspect findings.

Proposal to change widely accepted p-value threshold stirs reproducibility debate.