Inferential Statistics: Are all good sample random?

Are all good samples random?
Name Course Instructor Date
Are all good samples random? This is an opportunity to bring up opinion polling, which typically tries to obtain views from particular groups (e.g., men, women, older, younger, employed, unemployed, Democrat, Republican, etc.) and then “weights” the results by the prevalence in the population.
One of the purposes of inferential statistics is to estimate the unknown population characteristics, examining the information obtained from a sample obtained from the population. Random samples are preferred and they ought to be representative of the population being studied. As such, these random samples reflect observations of the population from which they originate, but still, there can be bias and sampling error where there is variation between samples from the same population. Randomization eliminates, or minimizes the sampling bias where the selection of samples is neutral or unbiased, but still, the samples may not represent all the characteristics of the population as is the case with opinion polls that over-represent or under-represent certain segments of a community or society.
In random sampling, the samples are chosen in such a way that all elements of the population have the same probability of being selected. The random samples are better than the non-randomized sampling as the latter is subjective. Selecting a sample of the population through random procedures then is better than the non-random samples as there is the likelihood that each and every one of the elements of the population is represented. In reality, there is a probability of a chosen sample affects whether the sample is truly representative of the population. In opinion polls, some people are easily re...