ADMS 2320 Study Guide - Midterm Guide: Statistical Parameter, Statistic, Null Hypothesis

• School: York University
• Department: Administrative Studies
• Course: ADMS 2320
• Professor: Marwan Hassan

Document Summary

Qualities desirable in estimators are unbiasedness, consistency and relative efficiency. Unbiasedness an unbiased estimator of a population parameter is an estimator whose expected value is equal to that parameter. Consistency if the difference between the estimator and the parameter grows smaller as the sample size grows larger. Relative efficiency - if there are two unbiased estimators of a parameter, the one whose variance is smaller is said to be relatively efficient. The probability 1 is the confidence level. Sampling error as the difference between an estimator and parameter. Statistical inference is the process by which we acquire information and draw conclusions about populations from samples. Objective of estimation is to determine the approximate value of a population parameter on the basis of a sample statistic. The width of the confidence interval estimate is a function of the population sd, confidence level and sample size. Null hypothesis is h0 (not enough evidence to infer h1 not rejecting null)