NURS341 Lecture Notes - Lecture 4: Null Hypothesis, Effect Size, Selection Bias
21 Mar 2018
School
Department
Course
Professor
Document Summary
If the sample is representative of the population, we can draw inferences from the sample about the population: bigger sample = the closer the mean and sd matched to the population. Inferential statistics allow researchers to make inferences about a population based on a sample. In order to make these inferences, researchers use the central limit theorem. Many say you need at least a sample size of 30: the sample should be randomly selected from the population of a finite variance. Two types of sampling: probability, simple random. Everyone in the sample has a chance of being selected. Randomly select the number of participants required. Population must be accessible so often not feasible: systematic sampling. Population must be accessible: stratified sampling. Divide population into subpopulations depending on variables of interest and then randomly sample from them: you know what proportion of people are in select groups, and you choose from different strata.
