We argue that statistical practice in the social and behavioural sciences benefits from
transparency, a fair acknowledgement of uncertainty and openness to alternative …

Mounting failures of replication in social and biological sciences give a new urgency to
critically appraising proposed reforms. This book pulls back the cover on disagreements …

Empirical research inevitably includes constructing a data set by processing raw data into a
form ready for statistical analysis. Data processing often involves choices among several …

Statistical power analysis provides the conventional approach to assess error rates when
designing a research study. However, power analysis is flawed in that a narrow emphasis …

There is a growing realization that reported “statistically significant” claims in scientific
publications are routinely mistaken. Researchers typically express the confidence in their …

Disputes between those holding differing political views are ubiquitous and deep-seated,
and they often follow common, recognizable lines. The supporters of tradition and stability …

Researcher degrees of freedom can lead to a multiple comparisons problem, even in
settings where researchers perform only a single analysis on their data. The problem is …

Decisions in statistical data analysis are often justified, criticized or avoided by using
concepts of objectivity and subjectivity. We argue that the words 'objective'and 'subjective'in …

We present explorable multiverse analysis reports, a new approach to statistical reporting
where readers of research papers can explore alternative analysis options by interacting …

Arguments that machine learning (ML) is facing a reproducibility and replication crisis
suggest that some published claims in research cannot be taken at face value. Concerns …