PSYC 202 Lecture Notes - Lecture 7: Central Limit Theorem, Normal Distribution, Statistical Population

• School: Queen's University
• Department: Psychology
• Course: PSYC 202
• Professor: William Nelson

Document Summary

Estimation: process of inferring a populatio(cid:374) para(cid:373)eter usi(cid:374)g data that"s i(cid:374) (cid:455)our sa(cid:373)ple, population parameter- any attribute of the population distribution ex. Mean, variants: shown as a greek letter, fixed, sample estimate will vary each time we do the sampling, use letters from english alphabet. If you increase the size of the sample we reduce the width of the sampling distribution. Standard error: standard error is used to measure the variations of a sampling distribution, standard error- standard deviation of the sampling distribution, x (cid:862)sig(cid:373)a su(cid:271)s(cid:272)ript (cid:454) (cid:271)ar(cid:863, standard error = , n= number of observations in sample. All from just your sample: your sample mean is an estimate of the expectation of the sampling distribution, your sample variance is an estimate of the variance of the sampling distribution. Se=(cid:2869). (cid:2870)(cid:2873)(cid:2874) (cid:2872)(cid:2868)=(cid:882). (cid:883)99 (cid:2868). (cid:2868)(cid:2873)(cid:4666)(cid:2870)(cid:4667),(cid:2871)9= (cid:884). (cid:882)(cid:884)(cid:885) (2)= two-tailed, 39=degrees of freedom (n-1)