PSY201H1 Lecture Notes - Lecture 3: Central Limit Theorem, Statistical Parameter, Sampling Error
28 Sep 2016
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Lecture 3 (september 27, 2016): standardized distributions & If all the scores in a data set are the same, what is the standard deviation: 0 (no variability) Inferential statistics: draw conclusions about a population based on a sample that we have, samples are usually less variable than the population we draw it from. Much less probably that we are going to pull values from the extremes of the distribution. So when calculating, we need to make a correction for this and we can do this because the bias is consistent. Find the ss for a sample: find deviation for each square: x-m, square each deviation: (x-m)2, add the squared deviations: (x-m)2. We divide by n-1 instead of n: variance of sample: s2 = ss/(n-1, standard deviation of a sample: s = [ss/(n-1)] Predicting sample standard deviation: take the largest and smallest deviation from the mean and take the mean of them.
