# Expressions to reconsider

**Published by:** [Jonas Ranstam BSc PhD](https://paragraph.com/@jonasranstam/)
**Published on:** 2022-11-18
**URL:** https://paragraph.com/@jonasranstam/expressions-to-reconsider

## Content

Many commonly used phrases are methodologically problematic or indicate methodological confusion. It is a good idea to check the used terminology. Here are a few common and annoying examples. Expression: Chi-squared tests were used to compare variables. Make sure that you distinguish between description and inference. Statistical hypothesis tests such as the Chi-squared test are used to test hypotheses about a population using data from a sample representing this population. P-values and statistical significance are not useful for describing the characteristics of the sample itself. Expression: Chi-squared tests and Fisher's exact test were used as appropriate. What do you consider appropriate? Expression: Independent samples T-tests. Several different t-tests have been developed for use with independent groups, e.g. Student's, Satterthwaite's, Welch's, Prien's t-tests and even Hotelling's T-test. Expression: Normality has been assessed using the Shapiro–Wilk test. No, a variable's distribution in the population can be tested using a statistical hypothesis test but cannot be "assessed" until the entire population has been observed, which is impossible for unobservable populations. Expression: Nonparametric data. A nonparametric hypothesis can perhaps be tested using a distribution-free test, but the phrase "nonparametric data" is just nonsense. Expression: Statistical difference. Aren't all differences statistical in some sense? Expression: No difference. The statement is ambiguous. Does it mean no clinically relevant difference or no statistically significant difference? Expression: Significant difference This statement is also ambiguous. Does the word significant refer to uncertainty (statistical significance) or relevance (clinical significance)? Expression: Independently associated variables The expression is common when estimating causal effects using a multiple regression model. However, these estimates can only be interpreted based on explicit assumptions regarding cause-effect relationships among the included variables. A multivariable analysis is unnecessary when all variables are assumed to be independent Schisterman EF, Cole SR, Platt RW. Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies. Epidemiology. 2009 Jul;20(4):488 - 495.

## Publication Information

- [Jonas Ranstam BSc PhD](https://paragraph.com/@jonasranstam/): Publication homepage
- [All Posts](https://paragraph.com/@jonasranstam/): More posts from this publication
- [RSS Feed](https://api.paragraph.com/blogs/rss/@jonasranstam): Subscribe to updates
- [Twitter](https://twitter.com/jonasranstam): Follow on Twitter