Biology 2244A/B Lecture Notes - Lecture 1: Simple Random Sample, Stratified Sampling, Gettysburg Address
16 Sep 2016
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Random: unbiased doesn"t characterize a certain part of the population want it to represent the whole not favoring a segment of the population. Large enough sample: lower chance of missing a group of people. Systematic favoritism in the data selection process, leading to misleading results: example: gettysburg address favoring certain words. Simple random sampling: all possible combinations (ex. samples) of size n from the population are equally likely: the best method of achieving a representative sample, idea of groups. Simple random sampling is unbiased no selection bias: shows that there is variation within samples. Sampling error: difference between a sample statistic and the true population parameter due to chance sample differences. Random sampling: each unit of the population has an equal chance of being selected: group being selection, restricting sample, groups have equal chances. Srs requires all groups to be equally likely and in random sampling and this violates the definition of a simple random sample.
