1. WELCOME TO BIOSTATISTICS! WELCOME TO BIOSTATISTICS! Course content

2. 1.Overview and Definitions 2.Experimental design 3.Data types and representation of data 4.Measurement of central tendency and dispersion 5.Normal distribution 6.Hypothesis testing 7.The t distribution and comparison of means Course content

3. 8.Calculation of 95% confidence intervals 9.Basic analysis of variance 10.Correlation and regression techniques 11.chi-squared tests 12.Mann-Whitney U tests (non-parametric methods) 13.Multivariate testing Course content

4. Population: The entire collection of items that forms the focus of a study. Random sample: Subset of the population that is randomly selected, and analysed appropriately for the information it contains. During selection, each item in the population had an equal opportunity to appear in the sample. An experiment: A deliberate course of action that is aimed at satisfying carefully stated objectives. Definitions

5. Response variable: That which is actually measured, as a function of the treatments Parameter: A measurement on a population that characterises one of it’s features. For example, the average of a specific response variable for a population - the average height of a group of people, the average weight etc. Definitions

6. Experimental unit (EU): The smallest division of experimental material to which a treatment can be assigned in a single act of randomization. Is a physical entity, e.g., cow, pot in the greenhouse, area of indigenous flora or can be a group of individuals, e.g., litter of pigs, number of plants in a tray. The EU is the smallest entity receiving a single treatment, provided two such entities could receive different treatments. Definitions

7. Sampling unit (SU): The unit of experimental material on which an observation is recorded. It is the physical entity on which the measurement is made. The experimental unit is often the same as the sampling unit. Often, the EU may be divided into two or more SU’s. EU = unit to which treatments are applied SU = unit upon which response variable is measured. Definitions

8. Experimental error: Variation which naturally exists among experimental units treated alike. Is a characteristic of all experimental material. It is the presence of random variation which makes statistics an integral part of all research endeavors. Is basically a collective term often used to describe variation resulting from all sources of variation unaccounted for in the experiment. Definitions

9. Sensitivity: The ability of an experiment to detect real differences. Confounding: The mixing together of effects. Effects of two independent variables on a dependent variable are said to be confounded when they cannot be distinguished from one another in the statistical analysis. Confounding often obscures the true effects of treatments on the response variable. Definitions

10. Replication: The assignment of more than one EU to the same treatment (or treatment combination). Is one complete set of treatments. Increasing the number of replications results in greater sensitivity by reducing the standard error of the difference between treatment means. Definitions

11. Randomization: This is the cornerstone of experimental design, and assures validity of the estimate. Refers to assignment of treatments to EU’s so that all units have an equal chance or receiving a treatment. It protects against systematic bias caused by subjective assignment of treatments, and also validates the statistical assumption that observations (or errors) are independently distributed random variables. Definitions

12. Local control: The techniques used to reduce or control experimental error,so that the over-all precision of the experiment is increased. This can be done by: 1)handling experimental material so that effects of inherent variability are reduced, 2)Refining experimental techniques for administering treatments and measuring responses 3)the most appropriate design structure is selected. Definitions

13. Why should we study and use stats? Definitions There are two main purposes: We summarise the important characteristics of a set of data We summarise the important characteristics of a set of data DESCRIPTIVE We generalise from a set of data describing a sample, to the larger population We generalise from a set of data describing a sample, to the larger population INFERENTIAL