# Stats

If you wanted to do a random sample of the students in the cafeteria, you need to look at the students in aggregate in the cafeteria, and not apply a stratified initial pass/approach (those who order Diet Pepsi with their lunch. Looking at the students in the cafeteria, and then applying a distinct subgroup for “Diet Pepsi drinkers” share the same characteristic and have been “stratified.

For this reason, stratifiying the Diet Pepsi drinkers from the cafeteria population you are eliminating the randomness of the population.

Regarding the statement “ A random sample is like a mini population whereas samples that are not random are likely to be biased” - using data from only part of the population of interest, is a sample of the population. There is no applied bias, because this is a random group of the population. If samples are not random, there could be a bias on their participation in the survey or census, for example.

(a) Simple Random Sample - this could be used for a neighborhood census, or anytime of census, where there could be a random sampling of the grouped population. (b) Stratified Sample - a group within a stated population as above - the students in the cafeteria who drink...

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