1. Sorting out statistical dilemmas P value or 95% CI Dr. Jayaprakash Muliyil MD MPH DrPH Professor, Community Health Christian Medical College, Vellore

2. Fact vs theory

3. Inductive logic vs deductive logic

4. David Hume’s contention Karl Popper’s solution

5. Truth Association PresentAbsent Study Association Present  α Absentβ 

6. Disease PresentAbsent Test Positiveab Absentcd Probability of no disease given that the test is positive =1-ppv = 1- a/(a+b)

7. Truth Association PresentAbsent Study Association Present  α Absentβ 

8. Statistical tests of significance Assumes null hypothesis is true Provides p values – Is the probability of the observed difference occurring purely by chance. – P value is a function of sample size

9. D+ D- Exposed 28 Unexposed 19 RR =2, P value >0.05 RR= 2, P value <0.05 D+ D- Exposed 2080 60 Unexposed 1090 615 25650 RR =2, P value <0.001 D+ D- Exposed 200800 Unexposed 100900

10. 95% Confidence Interval Estimating the location of population parameter from sample statistic. Frequency Difference in frequency Relative frequency (ratio)

11. Normal curve

12. Frequency 95% of sample values will lie within 2 standard errors of the population parameter Hence, when you create an interval of +- 2 standard errors around sample mean, 95% of the time, the population parameter will be included within it. Higher the precision – narrower the interval

13. Risk difference ( difference in frequency ) Null value = 0 If the risk difference confidence limits include 0 then the difference is not statistically significant

14. Risk ratio Null value =1 If the risk ratio confidence interval includes 1 then the risk estimate is not statistically significant.

15. Consider this MI No MI Smoking+ 10025 125 Smoking - 150300 450 250325 OR =8, 95% CI 2.87 to 21.21 Gastric cancer No gastric ca Smoking+ 70140 210 Smoking - 90280 370 160420 OR =1.56, 95% CI 1.05 to 2.29