This document provides definitions and explanations of key concepts in biostatistics and statistical hypothesis testing, including: - Types of data/variables, measures of central tendency, measures of dispersion - Descriptive vs inferential statistics, populations and samples - Assumptions of parametric tests, tests of normality, homogeneity of variance - Components of hypothesis testing, types of errors, significance levels and p-values - T-tests, ANOVA, within-subjects and between-subjects designs

1. DATA ANALYSIS

3. Definition of Biostatistics STATISTICS : F ield of study relating to the collection, classification, summarization, analysis and interpretation of numerical information. Definition of Statistics BIOSTATISTICS : Application of statistics to the analysis of biological and medical data.

6. sample should reflect the composition of the population of interest every person (or unit) in the population from which the sample is drawn has an equal probability of being chosen Sample as good estimators of population representative random

8. A variable is any measured characteristic or attribute that differs for different subjects. What is a Variable? For example, if the length of 45 leaves were measured, then length would be a variable.

9. Levels of Precision in Measurement names assigned to categories but no relation between the categories can be inferred Nominal Ordinal Interval Ratio values are ranked (put in order) distance between any two adjacent values is the same but the zero point is arbitrary similar to interval level but contains an absolute zero point Types of data/ variables

10. Example of ordinal scale Position Marks Matric. No. 5 60 123460 4 71 123459 3 72 123458 2 95 123457 1 98 123456

11. 1 2 3 4 5 6 7 8 9 10 interval same length Example of interval scale

12. Measures of central tendency Central tendency is the point at which the distribution of scores is centred . Three measures of central tendency: 1. Mode 2. Median 3. Mean

16. Measures of dispersion Dispersion refers to the variability of values in a data set i.e. the extent to which a set of values differ

20. Sampling Distribution μ = 10 9 8 7 4 3 2 1 5 6

21. Distribution of the sample means 11 9 9 8 12 7 11 6 10 5 10 4 9 3 10 2 8 1 Mean Sample

22. Frequency Sampling distribution of the sample means 3 2 1 8 9 10 11 12 Sample mean

24. Standard Deviation vs. Standard Error of the Mean (SEM) When to use which?

28. Skewness and Kurtosis

31. Kolmogorov Smirnov & Shapiro-Wilk

37. A tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation. Hypothesis Testing You have some claim about the parameter and you want to see whether the data supports the claim or not Hypothesis

45. 1 – β = power of a statistical test Power of a statistical test is the ability of a study to find a significant difference if indeed one exists. It is the probability that you will reject the null hypothesis when it is false Power of a statistical test

48. Independent t-test (SPSS output)