1. Introduction to Biostatistics Narumanas Korwanich Department of Community Dentistry, Chiangmai University Email: dncmi002@chiangmai.ac.th

2. Contents General concepts of statistics Scale of Measurement Consideration issues in statistics

3. Statistics Statistics is a science of data This involves collecting, classifying, summarizing, organizing, analyzing and interpreting numerical information

4. Research Steps Research Question Review literature Hypothesis formulation Research design Data collection Interpretation Conclusions

5. Sample * Sampling Techniques * Sample Size *Estimatio n *Hypothesi s testing Population

6. Sample Population Descriptive Statistics Inferential Statistics Generalization

7. Type of Statistics Application Descriptive statistics Inferential statistics

8. Type of Statistics Application Descriptive statistics utilizes numerical and graphical methods to look for patterns in a data set, to summarize the information revealed in a data set, and to present that information in a convenient form

9. Type of Statistics Application Inferential statistics utilizes data to make estimates, decisions, or generalization about a larger set of data

10. Fundamental Elements in Statistics Experimental unit : an object upon we collect data Population : a set of units that we are interested in studying Variables : a characteristic or properties of an individual unit Sample : a subset of the units of population

11. “Cola war” is the popular term for the intense competition between Coca-Cola and Pepsi displayed in their campaigns. Their campaigns have featured movie television stars, rock videos, athletic endorsements and claims of consumer preference base on taste tests.

12. Suppose, as part of a Pepsi marketing campaign, 1,000 cola consumers are given a blind taste test. Each consumer is asked to state a preference for brand A and brand B

13. Describe the population Describe the variable of interest Describe the sample Describe the inference

14. Scale of Measurement Nominal Scale Ordinal Scale Interval Scale Ration Scale

15. Nominal Scale A nominal scale uses names, numbers or other symbols to assign each measurement to one of a limited number of categories that cannot be ordered one above the other The values of the scale have no 'numeric' meaning in the way that you usually think about numbers Gender, Species

16. Ordinal Scale The assignment of numbers or symbols to identify ordered relations of some characteristic, the order having unspecified intervals Rating, SES, Quality (good, choice, prime) The rank order of anything

17. Numerical Scale The assignment of numbers or symbols to identify ordered relations of some characteristic Length or distance in centimeters, inches, miles

18. What kind of scale is this? External root resorption in millimeter Degree of agreement (most agree, fairly agree, no comment, fairly unagree, most unagree) Number of football player Composite resin colour Fluoride concentration in saliva Numerical Ordinal Nominal Numerical

19. Type of Variables Categorical : numerical, ordinal Numerical : continuous, discrete

20. Type of Variables Independent variables Dependent variables

21. The effect of 0.12% and 0.2% Chlorhexidine rinsing at the different time on number of microbial in the supragingival plaque What is independent and dependent variables? What kind of measurement scales are they?

23. The Moral of the Story : Cause and effect conclusions cannot generally be made based on a cross-sectional study

24. Who are those angry women? As of a questionnaire results to 100,000 US women asking about love, sex and relationship Only 4.5% return the questionaire

25. Who are those angry women? The women who responded were fed up with men and eager to fight them 91% of those who were divorced said that they had initiated the divorce

26. Who are those angry women? The Moral of the Story : An unrepresentative sample, even larger one, tells you almost nothing about the population

27. A statistical analysis maybe flawless, but it is not valid if data are gathered incorrectly. A statistical analysis may not even be possible if a question is formulated in such a way that a statistical hypothesis cannot be tested. Garbage in, garbage out