PhD / Research · Biostatistics and Data Science

Confidence intervals and p-values

A confidence interval describes the range of parameter values supported by a procedure under its statistical assumptions. A p-value quantifies how incompatible the observed data are with a specified null model.

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Confidence interval: effect and uncertainty

Original conceptual SVG · scalable
Null-effect referenceStudy AStudy BStudy CPosition = effect estimate • whiskers = uncertainty intervalInterpret intervals alongside design, bias and context

Illustrative effect sizes and confidence intervals, not research data. A reference line marks the null value in this abstract example.

Concept 01

Read an interval

A 95% frequentist confidence interval is built by a procedure that covers the fixed true parameter in 95% of repeated samples under its assumptions; it is not a 95% posterior probability for a specific interval.

Concept 02

Read a p-value

A p-value is not the probability that the null hypothesis is true, nor the probability the study result happened by chance.

Concept 03

Go beyond thresholds

Consider effect size, precision, multiplicity, study design, confounding, missing data and practical significance alongside the statistical result.

Education / safety note: Statistical estimates do not correct flawed data collection or justify claims beyond the sampled population.

Reference and next reading

Explore the original curriculum and publisher-hosted resources for full-depth reading; this note is an original schematic introduction, not an exhaustive chapter.

Official / publisher source: PubMed ↗Official / publisher source: PubMed ↗Official / publisher source: PubMed ↗