Instruction
1. Why then would a candidate rely on random sampling to make these assumptions? Random sampling is defined as “every unit of the population has the same probability of being selected into the sample (Black, 2017). The most difficult part about random sampling is knowing that your sample accurately represents the total population. A little analysis needs to be done in order to understand if the sample gives a clear representation of the entire data. Sometimes a random sample is necessary because of the large size of the overall population. Finding ways to analyze the data so you can be confident in the results helps you use the sample and make decisions on where to go next. If the mayor wants to know what his or her chances are of winning his staff would need to further understand the sample and its characteristics. Black, Ken. Business Statistics: For Contemporary Decision Making, 9th Edition. Wiley, 2016-09-26. VitalBook file.
2.Many of life's events share the same characteristics as the central limit theorem because there are many different variables with life's events just like central limit theorem. Also, when taking a large sample of something it allows you to figure out predictions. The reason that estimations and confidence intervals are important are because they allow you to get an idea for how well something is doing so that you can improve upon it or change things if they are not doing well. Systematic sampling could be biased based upon how big or little the group is and what type of sample is being given to that group. There are many factors when you get a result from a sampling. The roles that confidence intervals and estimation plays in selection sample size depends on how big or small the group is. It can be measured easier with a smaller group of people but when its a larger group of people it could be more precise.
3. A researcher can introduce bias "when selecting items in framing the sample there is the use of own discretion" which is a good point. How can a researcher avoid this pitfall? Is it the systematic sampling that is biased or merely the selection criteria? How does that bias impact the results of the study
4. Regarding the election surveys, it is important for the researcher to evaluate the survey to provide accurate results possible. One way to evaluate is to determine what sampling methods they used, either probability or non-probability. As we have learned this week, the only way to make correct statistical inferences from sample to a population is through the utilization of probability sample. Surveys that use non-probability sampling methods are subject to serious, perhaps unintentional, biases that may render the results meaningless and invalid. Consider the 1948 U.S. presidential election where major pollsters predicted confidently that Thomas E. Dewey will win against Harry S. Truman. But the actual results of the election are turned out to be almost exactly reversed. So, why were the pollsters so wrong? Intent on discovering the source of the error, the pollsters found that their use of a nonprobability sampling method was the culprit. As
a result, polling organizations adopted probability sampling methods for future elections.