PSYC 21621 Lecture Notes - Lecture 15: Central Limit Theorem, Variance, Statistic
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What about probability and samples: probability models, so far, we have used probability to discuss a single observation, p(heads, p(1st patient has depression, p(green m&m) In statistics we are not concerned with single observation, but the observations from samples. Inferential statistics: we want to infer from the observations of our sample to the population. Sampling error: sampling error is the natural discrepancy, or the amount of error, between a sample statistic and its corresponding population parameter, not a mistake: it"s expected. Sampling variability: taking a sample is a random event, because of that, computing the sample mean, computing the sample standard deviation, computing any statistic, all random events . Central limit theorem: 3 rules: rule 1: shape, the shape of the distribution of sample means tends to be normal. It is what we expect the mean to be based on the population: rule 3: variability, the standard deviation of the distribution of sample means is called the.