82. Geoff Cumming: p-values, estimation, and meta-analytic thinking

BJKS Podcast

Geoff Cumming is an Emeritus Professor at La Trobe University. In this conversation, we discuss his work on New Statistics: estimation instead of hypothesis testing, meta-analytic thinking, and many related topics.
Support the show: https://geni.us/bjks-patreon
Timestamps
0:00:00: A brief history of statistics, p-values, and confidence intervals
0:32:02: Meta-analytic thinking
0:42:56: Why do p-values seem so random?
0:45:59: Are p-values and estimation complementary?
0:47:09: How do I know how many participants I need (without a power calculation)?
0:50:27: Problems of the estimation approach (big data)
1:00:08: A book or paper more people should read
1:02:50: Something Geoff wishes he'd learnt sooner
1:04:52: Advice for PhD students and postdocs
Podcast links

• Website: https://geni.us/bjks-pod
• Twitter: https://geni.us/bjks-pod-twt

Geoff's links

• Website: https://geni.us/cumming-web
• Google Scholar: https://geni.us/cumming-scholar
• Mastodon: https://nerdculture.de/@thenewstats

Ben's links

• Website: https://geni.us/bjks-web
• Google Scholar: https://geni.us/bjks-scholar
• Twitter: https://geni.us/bjks-twt

References/links
Dance of the p-values: https://www.youtube.com/watch?v=5OL1RqHrZQ8
Significance roulette: https://www.youtube.com/watch?v=OcJImS16jR4
Episode with Simine Vazire (SIPS): https://geni.us/bjks-vazire
Coulson, ...(2010). Confidence intervals permit, but don't guarantee, better inference than statistical significance testing. Front in Psychol.
Cumming & Calin-Jageman (2016/2024). Introduction to the new statistics: Estimation, open science, and beyond.
Cumming (2014). The new statistics: Why and how. Psychol Sci.
Cumming & Finch (2005). Inference by eye: confidence intervals and how to read pictures of data. American Psychol.
Errington, ... (2021) Reproducibility in Cancer Biology: Challpenges for assessing replicability in preclinical cancer biology. eLife.
Errington, ... (2021) Investigating the replicability of preclinical cancer biology. eLife.
Finch & Cumming (2009). Putting research in context: Understanding confidence intervals from one or more studies. J of Pediatric Psychol.
Hedges (1987). How hard is hard science, how soft is soft science? The empirical cumulativeness of research. American Psychologist.
Hunt (1997). How science takes stock: The story of meta-analysis.
Ioannidis (2005). Why most published research findings are false. PLoS Medicine.
Loftus (1996). Psychology will be a much better science when we change the way we analyze data. Curr direct psychol sci.
Maxwell, ... (2008). Sample size planning for statistical power and accuracy in parameter estimation. Annu Rev Psychol.
Oakes (1986). Statistical inference: A commentary for the social and behavioural sciences.
Pennington (2023). A Student's Guide to Open Science: Using the Replication Crisis Reform Psychology.
Rothman (1986). Significance questing. Annals of Int Med.
Schmidt (1996). Statistical significance testing and cumulative knowledge in psychology: Implications for training of researchers. Psychol Methods.

Chapters

1. A brief history of statistics, p-values, and confidence intervals (00:00:00)

2. Meta-analytic thinking (00:32:02)

3. Why do p-values seem so random? (00:42:56)

4. Are p-values and estimation complementary? (00:45:59)

5. How do I know how many participants I need (without a power calculation)? (00:47:09)

6. Problems of the estimation approach (big data) (00:50:27)

7. A book or paper more people should read (01:00:08)

8. Something Geoff wishes he'd learnt sooner (01:02:50)

9. Advice for PhD students and postdocs (01:04:52)