STAT 110 1nd Edition Lecture 1 Outline of Current Lecture I. Sampling FramesII. Error in Sampling III. Stratified Random SamplingCurrent LectureI. Sampling Framesa. We draw samples from populations in an attempt to represent the entire population with a smaller, more manageable group of participants. However, since it is usually impossible to number every subject in the population in order to gather a sample, we take samples from a sampling frame. A sampling frame is a list of population subjects from which we select our sample. II. Errors in Samplinga. There are two different types of error that happen in sampling: sampling errors, and non-sampling errors:i. Sampling errors1. Bad Sampling Methodsa. The two types of bad sampling methods are convenience samples and voluntary response samples. 2. Random Sampling Errora. Random sampling error is caused by chance, and it measures the difference between a population parameter and a sample statistic. Random sampling error is the only error accounted for by the margin of error. ii. Non-sampling errors1. Processing errors occur when a mistake is made in data entry 2. Poorly worded questions can lead to bias, which leads to error 3. Response error occurs when a respondent gives false information through lying, false memory, or bias. 4. Non-response error occurs when a respondent fails to answer a question III. Stratified Random SamplingThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.a. As researchers, when we are interested in seeing how different groups of people will respond to the same question, we often utilize stratified random sampling.i. In order to select a stratified random sample, we must first divide our sampling frame into clearly separate groups called strata. ii. Then, we simply take a simple random sample from each stratum. iii. The combination of participants from each stratum will make up our whole