This document discusses some statistical issues to consider when conducting research on endocrinology and exercise. It highlights the importance of sample size considerations, accounting for all statistical components, avoiding pseudoreplication in data analysis, and being careful when analyzing change scores and normalized variables. The document emphasizes targeting the mean treatment effect using ANCOVA and accounting for individual differences in change. Researchers are advised to read literature from other fields to be aware of ongoing statistical debates and approaches.

1. Significance of Statistics
Some Statistical Pitfalls in Research
on Endocrinology and Exercise
Greg Atkinson (Teesside University, UK)
Nottingham Trent University –
Physical Activity and the Endocrine System
LEAVE SPACE
HERE
LEAVE SPACE
HERE

2. Just some statistical issues to consider and
main pointers
 Sample Size considerations
 Be as pragmatic as possible
 Rationalise ALL components if you do an estimation
 Unit of analysis considerations
 Beware “pseudoreplication”
 Be careful with normalised variables (especially ratios)
 Analysing change
 Be careful with % changes
 Target the mean treatment effect with ANCOVA
 Be careful with individual differences in change
LEAVE SPACE
HERE
LEAVE SPACE
HERE

3. Sample size considerations
Be as pragmatic as possible
Lakens, D. Sample Size Justification, https://psyarxiv.com/9d3yf/
LEAVE SPACE
HERE
LEAVE SPACE
HERE

4. Sample size considerations
Report ALL components
Sample size
Target effect size
Variance
Power
Alpha
LEAVE SPACE
HERE
LEAVE SPACE
HERE
https://www.sciencedirect.com/science/article/pii/S1466853X05000714
https://pubmed.ncbi.nlm.nih.gov/24806703/

5. Unit of analysis considerations
Pseudoreplication
Neglecting to model replicate
measurements properly
Inflates sample size
Biases effects and correlations
Ecological Fallacy
LEAVE SPACE
HERE
LEAVE SPACE
HERE
https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005282

6. Unit of analysis considerations
Normalised variables
LEAVE SPACE
HERE
LEAVE SPACE
HERE
https://pubmed.ncbi.nlm.nih.gov/22760546/
Mathematical
coupling

7. Analysing change
Percentage changes
LEAVE SPACE
HERE
LEAVE SPACE
HERE
https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-1-6

8. Analysing change
Target mean treatment effect
LEAVE SPACE
HERE
LEAVE SPACE
HERE
Change or Follow-up is
the outcome
Group or condition is a
fixed effect
Baseline values are a
covariate
Resulting mean
treatment effect is
control group adjusted
and baseline imbalance
adjusted.
P<0.05
P>0.05

9. Analysing change
Individual responses
LEAVE SPACE
HERE
LEAVE SPACE
HERE

10. In Summary
 Beware of pitfalls – even when doing analyses that are
considered “straightforward”
 e.g. Correlations
 Read the applied statistical literature even if not in your “field”
 Ecology, Psychology, Neuroscience, Epidemiology, etc.
 Follow statisticians on Twitter, e.g.
 Stephen Senn
 Darren Dahly
 Andrew Vickers
LEAVE SPACE
HERE
LEAVE SPACE
HERE
Expect massive debates to be going on
about the statistical approaches you
and others think are the done deal!

11. Significance of Design and Statistics
Thanks for listening
Greg Atkinson (Teesside University, UK)
Nottingham Trent University –
Physical Activity and the Endocrine System
LEAVE SPACE
HERE
LEAVE SPACE
HERE