International Journal of Endocrinology and Metabolism· 2012Q3
Normality Tests for Statistical Analysis: A Guide for Non-Statisticians
- 4,230citations
- Q3SCImago
- 2012year
Short summary
This commentary overviews normality testing in SPSS for statistical analysis, highlighting that ~50% of published articles contain statistical errors, often stemming from unverified normality assumptions crucial for parametric tests.
AI-generated from the title and abstract; the full text is not read.
Key points
- Around 50% of published scientific articles contain at least one statistical error.
- The assumption of normality is a prerequisite for many statistical procedures, particularly parametric tests.
- Failure to check for normality can compromise the validity of statistical findings.
- This commentary focuses on guiding non-statisticians through normality testing using SPSS.
AI-generated from the title and abstract; the full text is not read.
Abstract
Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.
The authors' abstract, as published at the source. International Journal of Endocrinology and Metabolism, 2012 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Cardiology and Cardiovascular Medicine
Cardiology and Cardiovascular MedicineMedicine