BMGT 230 Lecture Notes - Lecture 3: Sampling Frame, Sampling Error, Sample Size Determination

• School: University of Maryland
• Department: Business and Management
• Course: BMGT 230
• Professor: Radu Lazar

Document Summary

Bmgt230 lecture 3 notes feb 1st. Population - a group of individuals of interest: ofte(cid:374), you do(cid:374)"t have a(cid:272)(cid:272)ess to the e(cid:374)ti(cid:396)e populatio(cid:374) Sample a part of the population. Parameter a characteristic that we want to learn about it is generally unknown. Statistic a known estimate of a parameter from a sample. Bias when the sample is not representative of the population; it is always bad: systematic/ selection bias when certain individuals in the population are omitted from the sample. Sampling frame the part of the population that can be reached: non-response almost always a problem, when people fail to respond, response people lie a(cid:374)d you (cid:272)a(cid:374)"t do a(cid:374)ythi(cid:374)g a(cid:271)out it. Randomization to get rid of bias we must dp it. Sampling error sample to sample variation. Example 1: false, true, true, false. Example 2: population all frequent flyers, sampling frame same size. Non statistical: convenience taki(cid:374)g the (cid:862)easiest(cid:863) sa(cid:373)ple.