1. The New Statistics: Why & How Corey Mackenzie, Ph.D., C. Psych

2. http://www. latrobe. edu. au/scitecheng/about/staff/profile

3. Outline Need for changes to how we conduct research 3 “new” solutions
Three threats to research integrity
Shift from Null Hypothesis Sig Testing (NHST)
3 “new” solutions
Estimation
Effect sizes
Meta-analysis

4. 1st change to how we do research: Enhance research integrity by addressing three threats

5. Threat to Integrity #1 We must have complete reporting of findings
Small or large effects, important or not
Challenging because journals have limited space and are looking for novel, “significant” findings
Potential solutions
Online data repositories
New online journals
Open-access journals

6. Threat to Integrity #2
We need to avoid selection and bias in data analysis (e.g., cherry picking)
How?
Prespecified research in which critical aspects of studies are registered beforehand
Distinguishing exploratory from prespecified studies

7. Threat to Integrity #3
We need published replications (ideally with more precise estimates than original study)
Key for meta-analysis
Need greater opportunities to report them

8. 2n change to how we do research: stop evaluating research outcomes by testing the null hypothesis

9. Problems with p-values
In April 2009, people rushed to Boots pharmacies in Britain to buy No. 7 Protect & Perfect Intense Beauty Serum. They were prompted by media reports of an article in the British Journal of Dermatology stating that the anti-ageing cream “produced statistically significant improvement in facial wrinkles as compared to baseline assessment (p = .013), whereas [placebo-treated] skin was not significantly improved (p = .11)”. The article claimed a statistically significant effect of the cream because p < .05, but no significant effect of the control placebo cream because p > .05. In other words, the cream had an effect, but the control material didn’t.

10. Problems with NHST Kline (2004) What’s Wrong with Stats Tests
8 Fallacies about null hypothesis testing
Encourages dichotomous thinking, but effects come in shades of grey
P = .001, .04, .06, .92
NHST is strongly affected by sample size
Null Hypothesis Significance Testing

11. Solution #1
Support for Bill 32 is 53% in a poll with an error margin of 2%
i.e., 53 (51-55 with 95% confidence) vs
Support is statistically significantly greater than 50%, p < .01

12. Solution #2 http://en.wikipedia.org/wiki/Effect_size
G*Power

13. Solution #3 Meta-analysis
P-values have no (or very little) role except their negative influence on the file-drawer effect
Overcomes wide confidence intervals often given by individual studies
Can makes sense of messy and disputed research literatures

14. Why do we love P? Suggests importance We’re reluctant to change
Confidence intervals are sometimes embarrassingly wide
9 ±12
But this accurately indicates unreliability of data

15. Why might we change? 30 years of damning critiques of NHST
6th edition of APA publication manual
Used by more than 1000 journals across disciplines
Researchers should “wherever possible, base discussion and interpretation of results on point and interval estimates”

16. Epi Example