1. Caveats when using statistics Lessons I have learnt so far Week 14

2. Important issues in statistics 1.Effect sizes 2.Accuracy of estimation 3.Power 4.Research methodology 5.Don’t underestimate visualizations.

3. Randomness If there was no randomness, we would not need any inferential statistics.

4. Three Common Misinterpretations of Significance Tests and p-values 1.The p-value indicates the probability that the results are due to sampling error or “chance.” 2. A statistically significant result is a “reliable” result. 3. A statistically significant result is a powerful, important result.

5. Effect sizes

6. Effect sizes are important Researchers published a randomized control trial where he showed that giving disadvantaged children vitamin supplements improved IQ. Simple biological reason: nutrients are needed for brain development, and malnourished children lack such nutrients Criticisms: –Effect too small Eysenck (1991). Raising IQ through vitamin and mineral supplement. Pers Ind Diff.

7. But even effect sizes cannot tell anything about the practical importance of any finding Schoenthaler et al. (1991). The importance of the result, of course, depends on the issue at hand, the theoretical context of the finding, etc.

8. Accuracy of estimation is important

9. The real problem: arbitrariness Often, it is unsatisfying to conclude whether a treatment worked or not. A p-value forces you to accept/reject the null.

10. Which research would you favor?

11. Power is important

12. Power The chance of finding a statistically significant effect, suppose the effect is real. Whether a finding is replicable or not depends on –Power of each study –Whether the effect is real

13. The real problem: Power is confounded Power depends on sample size (and alpha). Any small effect will be significant when N  ∞ Facebook study N = 689,003; d ≈ 0, p <.001 Kramer et al. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proc Natl Acad Sci.

14. Where do you go from SRM?

15. Research methodology The numbers are only as good as the method used to produced them. SRM II will cover methodology in detail.

16. Don’t neglect visualizations But accuracy is king!

18. Exam preparation

22. Data analysis project Q & A

23. Operationalization vs. Abstraction Operationalization Abstraction

24. “ANOVA seems unsophisticated compared to regression” The right tool to answer the wrong question is the wrong tool.

25. I hate to nag, but… Do not write p =.00 or p = 1.00 Means and SDs should be reported together

26. Submission Email it to me: kai.chan@ashoka.edu.in