PSY 2106 Lecture Notes - Lecture 9: Central Limit Theorem, Sampling Distribution, Sampling Error

• School: University of Ottawa
• Department: Psychology
• Course: PSY 2106
• Professor: Heather Poole

Document Summary

But it"s not really a problem since we are drawing from large populations. Remember, we use samples to estimate populations. Population distribution: distribution of individuals scores (n=1) Sampling distribution of the mean: distribution of sample means (n>1) Standard deviation of this distribution is called standard error. The sampling distribution of the mean is a crucial concept in statistics. Variability of a statistic from sample to sample due to chance. For example, we know our population distribution of iq scores is nd (100, 15) But each sample of people we may take, won"t have exactly a mean of 100 it may be 102, 96, 5 etc. Thus any sample statistic value will be error prone" (different doesnt mean we have made a mistake - it just means there"s random variability we can"t explain through our manipulation. Describes the distribution of sample means in terms of its centre, width and shape. Center: on average, sample mean = population mean.