1. Statistics 1 Simon Carley

2. What do you need to know? 3. This is not a statistics course! 4. Objectives Types of data What are you likely to meet in the results? What the hell is a power study? 5. Types of data Continuous Categorical Ordinal 6. Continuous Any value possible Height? Blood pressure? Amylase? PEFR? Heart rate? 7. Continuous - normal or not? Whats a normal distribution? 8. Tests for continuous data continuous + normal = t-tests (mean)multiple means - ANOVAcontinuous + not normal = rank tests (median) Mann-Whitney U test Wilcoxon signed rank test Kruskall-Wallishttp://www.graphpad.com/www/book/choose.ht m 9. Categorical data Data that appears as a category No values between categories pregnant VQ scan 10. Ordinal data Like categorical but evenly distributed Pain ladder Useful to spot as better stats can be used 11. Categorical data categorical data = Chi squared test categorical data with small numbers (less than 5 in a group) = Fishers exact test 12. Things I need to know p-values confidence intervals 13. p-value If there is no difference................etc. 14. p-value Start with the null hypothesis in mind.If the p value is less than that considered statistically and clinically important then we reject the null hypothesis and accept that there is a difference.If the null hypothesis is true then the chance of finding a result this unlikely (or more unlikely) is p=0.****. 15. confidence intervals how close is the result to the true population value? Used when aggregating data tell me what the average height of junior doctors is? 16. confidence intervals how close is the result to the true population value? Used when aggregating data We sample 300 doctors and find anaverage height of 174cm....but there is a range. How different might the true value be? 17. Summary Understand the data types. This will help you know if the right sort of tests have been used. Know the definitions of p values and confidence intervals. 18. EBEM Podcast Simon Carley Manchester England