Importance of Sample Size in Statistics
Sample size relative to the size of the target population is an important factor in determining which kinds of statistical procedures can be used to test hypotheses. Generally, larger populations with large enough sample sizes are more generalizable than smaller populations and relatively small sample sizes.
Conduct a Google search for a sample size calculator. Experiment with various population sizes and sample sizes at different confidence levels.
Then, imagine a scenario within a population you might want to research. Estimate the size of the population. Develop an original response by sharing your research scenario and your estimate of the population size. Apply the sample size calculator to answer these questions:
Based on the size your population, how big does your sample need to be to use parametric procedures at a 95% confidence level?
Why might a smaller sample size, or not a larger enough one, necessitate the use of nonparametric statistical procedures? What does nonparametric mean in this case?
Importance of Sample Size in Statistics
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Importance of Sample Size in Statistics
Sample Size to use parametric procedures at a 95% confidence level
The sample size determines which statistical method can be applied and how effective it will be. Most statistical techniques can be used for both big and small samples. However, the sample size impacts how much information statistical methods can reveal about the population from which the sample was drawn (Ryan, 2019). The sample size was 646 based on the first sample size of my population, which was 850,000. It indicates that 646 samples or more are required to get a 95% confidence level and that the actual value is within +/- 5% of the tested or studied value. The error rate was 9.60%. There is a 95% likelihood that the actual value in this situation will be within +/- 9.60% of the observed survey value. There was a 1.96 Z score. However, when I computed the sample size for the precise population of 4,000,000 with the same 95% confidence level, it was 646, just one point more than the calculation for an 850,000-person population. In employing parametric techniques with a 95% confidence level for ...