PSYC 2021 Lecture Notes - Lecture 7: Frequency Distribution, Central Tendency, Standard Deviation

• School: York University
• Department: Psychology
• Course: PSYC 2021
• Professor: Margarete Wolfram

Document Summary

Samples, populations and the distribution of sample means. Sampling error: the discrepancy, or amount of error, between a sample statistic and its corresponding population parameter. (cid:862)sa(cid:373)ples a(cid:396)e (cid:448)a(cid:396)ia(cid:271)les(cid:863) they are not all the same: two separate samples from the same population samples will be dif, they will have dif scores, sample means, and dif invidiuals. This theorem describes the distribution of sample means by identifying 3 characteristics: shape, central tendency, variability. The shape of the distribution of sample means. The mean of the distribution of sample means: the expected value of m. The average of all the sample means = population mean: this mean value is called the expected value of m ^ The sample mean is an example of an unbiased statistic: on avg, the sample statistic produces a value that is = to the population parameter. The standard deviation for the distribution of sample means: identified by the symbol and is called the standard error of m.