PSYC 2100WQ Lecture Notes - Lecture 10: Central Limit Theorem, Bias Of An Estimator, Developmental Disorder
4 Oct 2018
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Document Summary
The simplest distribution with a fixed mean and variance (least assumptions) Central limit theorem: the sum of many random variables will have an approximately normal distribution (under fairly general conditions) Represents the probabilities of the complete set of values that can occur in a population. Represents the frequency of scores in a sample. Represents the possible values that the mean of a sample can take for a specific sample size n. Shape of distribution is approx normal due to central limit theorem. As sample n goes up shape of distribution of sample means looks more like a normal distribution. This doesn"t depend on the shape of the population distribution. Shape of distribution of sample means is normal for large enough sample size. If error bars overlap-difference may be due to chance. If error bars don"t overlap- difference might be real.
