Survey Sampling – Introduction When it’s not feasible to study an entire target population, a simple random sample is the next best option; with sufficient sample size, it satisfies the assumption of independent and identically distributed variables. And perhaps even more important, it will tend to be nicely representative for the population with regard to […]
Sampling is at the very core of statistical tests: drawing conclusions on research populations based on (small) samples from those populations. Basically all statistical tests quietly assume that the data you’re analyzing are a simple random sample from your population. This assumption being ignored is the very reason why political polls are often widely off and research findings can’t be […]
A sampling distribution is the frequency distribution of a statistic over many random samples from a single population.Sampling distributions are at the very core of inferential statistics but poorly explained by most standard textbooks. The reasoning may take a minute to sink in but when it does, you’ll truly understand common statistical procedures such as ANOVA or a chi-square test. […]
Introduction Popular statistical procedures such as ANOVA, a chi-square test or a t-test quietly rely on the assumption that your data are a simple random sample from your population. Violation of this assumption may result in biased or even nonsensical test results and few researchers seem to be aware of this. Plenty of reasons for a brief discussion of simple […]
By default, every case in your data counts as a single case. However, you can have each case count as more or less than one case as well. This is called weighting. For instance, the first case in your data may count as 2 cases and the second one as .5 cases. These numbers, the case weights, […]
