ACTB40 Lecture Notes - Lecture 2: Data Analysis, Internal Validity, Dependent And Independent Variables
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Poling the whole population is not practical or efficient, thus require a sample size and try to eliminate as much bias as possible. randomization does not always eliminate bias. Ex- pull balls out of hat, blue and red, can pick out 5 balls and they could all be red- this does not represent the whole population. Population vs. sample population- is the collection of the events we are interested in. totality of the course. sample= subset of these events. Use this data to make inferences on the population. If you are only concerned with your tutorial did, then that is your population size. Ex- voting behaviours- 7000 (this is population size), sent out 500 surveys, the ones you receive back are the sample size- roughly 100. Vs. inferential stats. continuing with example- examining 100 people examined. Describing the data- how its shaped the quantitative values. Inferential stats- using sample data to infer about the whole population.
