EEMB 146 Lecture Notes - Lecture 2: Cluster Sampling, Block Design, Dependent And Independent Variables
4 Apr 2018
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The scope of inference refers to the population to which inference (conclusions) can reasonably be drawn based on the study. When sampling from a population, we want to find an efficient way of obtaining a precise estimate of the parameter. Biased can happen when you aren"t really sampling from the entire population about which you want to make inference (draw conclusions) Stratified random sampling divide a population into smaller groups known as strata the strata are formed based on members" shared attributes or characteristics. A sampling technique where the entire population is divided into group, or clusters, and than a random sample of these clusters are selected and. Randomized block design the experimenter divides subjects into subgroups called blocks, such that the variability within blocks is less than the variability between blocks (aka. Subject within the block is as similar as possible) Then, subjects within each block are randomly assigned to different treatment conditions.
