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## Guidelines on statistics for researchers using laboratory animals: the essentials

There is a growing concern that flawed statistics and deficient reproducibility in biomedical studies results in an unethical waste of animals in research. This review by Romain-Daniel Gosselin aims to provide guidelines in biostatistics for researchers, based on frequently observed mistakes, misuses and misconceptions as well …

## Bayes’ Theorem: How do we weigh evidence to make decisions?

In this clearly and graphically explained educational video, Amber Biology offers an intuitive introduction to Bayes’ Theorem, using biomarkers and ovarian cancer testing as an example for life science applications of the Bayesian approach. As the authors state, “Bayes’ Theorem is something that every scientist, physician and …

## Four simple ways to increase power without increasing the sample size

Underpowered experiments have three problems: true effects are harder to detect, the true effects that are detected tend to have inflated effect sizes and as power decreases so does the probability that a statistically significant result represents a true effect. To increase power, many researchers only …

## P values in display items are ubiquitous and almost invariably significant: A survey of top science journals

P values represent a widely used, but pervasively misunderstood and fiercely contested method of scientific inference. Display items, such as figures and tables, often containing the main results, are an important source of P values. In this article, published in PlosONE, Ioana A. Cristea and John …

## Simple statistics

This website was published by Tom MacWright and contributers to provide a library of statistical methods in readible JavaScript. This detailed descirption of codes facilitates usage of statistics in browsers and servers. Simple statistics is bridging the power of statistics with the code and enables better …

## A systematic review of sample size and power in leading neuroscience journals

Adequate sample size is key to reproducible research findings: low statistical power can increase the probability that statistically significant results are false positive. To increase data robustness and reproducibility, journals started to implement measurements such as reporting checklists. In this paper, Carter et al. conducted a …

## Abandon Statistical Significance

In science publishing and many areas of research, we have reached a ‘binary decision rule’ in which any result is first required to have a p-value that surpasses the 0.05 threshold and only then is consideration given to such results as strong evidence in favor of …

## Standardized mean differences cause funnel plot distortion in publication bias assessments

Meta-analysis is a complex statistical method which involves synthesis of data from relevant studies to devise an effect size or a conclusion and has increasingly been recognized as an important tool in the field of biomedical life sciences. Meta-analyses often include an assessment of publication bias …

## When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment

Null hypothesis significance testing (NHST) is linked to several shortcomings that are likely contributing factors behind the widely debated replication crisis in psychology and biomedical sciences. In this article, Denes Szucs and John P. A. Ioannidis review these shortcomings and suggest that NHST should no longer …