P values, confidence intervals, or confidence levels for hypotheses?
Michael Wood

TL;DR
This paper critically examines the use of p values and confidence intervals, proposing a probabilistic definition of confidence to improve hypothesis assessment clarity and suggesting methods to convert between formats.
Contribution
It introduces a straightforward probabilistic definition of confidence and discusses its application, contrasting it with traditional methods and providing conversion techniques.
Findings
Confidence approaches are generally more transparent and useful.
P values can be used to derive meaningful confidence statements.
Context determines whether p values or confidence intervals are more appropriate.
Abstract
Null hypothesis significance tests and p values are widely used despite very strong arguments against their use in many contexts. Confidence intervals are often recommended as an alternative, but these do not achieve the objective of assessing the credibility of a hypothesis, and the distinction between confidence and probability is an unnecessary confusion. This paper proposes a more straightforward (probabilistic) definition of confidence, and suggests how the idea can be applied to whatever hypotheses are of interest to researchers. The relative merits of the different approaches are discussed using a series of illustrative examples: usually confidence based approaches seem more transparent and useful, but there are some contexts in which p values may be appropriate. I also suggest some methods for converting results from one format to another. (The attractiveness of the idea of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMeta-analysis and systematic reviews · Forecasting Techniques and Applications
