STAT151 Lecture Notes - Lecture 1: Random Assignment

• School: University of Alberta
• Department: Statistics
• Course: STAT151
• Professor: Alireza Simchi

Document Summary

Census: special sample that includes everyone and sample the entire population. Statistic: summary that is found from data in a sample. Parameters: describes a characteristic of the population; often found with population inference. Types of conclusions: population inference: generalizing results from a sample to an entire population. => require random sampling; non-random sampling leads to biased results: casual (cause & effect) inference: difference in responses caused by the difference in treatments when comparing the results from two treatment groups. => require random allocation ( ) and random assignment; or lurking variables could create change. The investigator observes individuals and measures variables of interest but does. => cannot make casual inferences from observational studies: randomized, comparative experiment. A study design that allows us to prove a cause-and-effect relationship: manipulates factor levels to create treatments, randomly assigns subjects to these treatment levels, compares the responses of the subject groups across treatment levels.