"The larger the sample, the more reliable the results." Do you agree or disagree with this statement? Explain.
Larger samples can provide for a greater chance of finding a significant difference. The larger sample size will maximize the chance of being able to uncover a specific mean difference. Since a larger sample offers the opportunity to reflect a population, for example, this can increase the chance of significance. “The size of our sample dictates the amount of information we have and therefore, in part, determines our precision or level of confidence that we have in our sample estimates. An estimate always has an associated level of uncertainty, which depends upon the underlying variability of the data as well as the sample size.” (Marley, 2018) When more variables exist, this can increase uncertainty along with a larger sample decreasing uncertainty. When developing a sample, it is important to remain unbiased, to be inclusive and to represent a population appropriately. A larger sample does not always mean that our results will be reliable. The greater the sample, the lower the confidence interval. When the sample size increases, then the population mean of the sample will be the same as the distribution being sampled. When the sample increases then the standard deviation of the means will decrease and vice versa.
Again, the data retrieved from a sample can be impacted by the way in which a sample is formed. The process of sampling includes defining the target population in which one would like to learn information. Researchers will then determine a sampling frame, sampling unit, and method. The next step is when the researcher will determine the sample size and how they will select the sample. Also, it is important to remember that larger sample sizes may cost more time and money.
Marley, S. (2018, June 19). The Importance and Effect of Sample Size. Retrieved September 9, 2018, from https://select-statistics.co.uk/blog/importance-effect-sample-size/
by EssayRoyal, Dec. 6, 2019, 5:45 p.m.