Importance of Sample Size in Statistics

Please respond to each of the three post below in three separate paragraph.(eg 1 paragraph for post 1, 1 for post 2 and 1 for post 3)
Guide for the posts: 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?
Post 1
In my scenario, I would be interested in researching how the classroom teachers in my district feel about instructional technology, their technology skills, and their students' technology skills. In my district, there are around 6,000 classroom teachers. According to the sample size calculator on SurveyMonkey.com, my sample size would need to be 362 based on a confidence level of 95% and a margin of error of 5%.
Oxford Reference (n.d.) defines nonparametric statistics as, "A branch of statistics used on data that are significantly skewed. The data distribution cannot be characterized by a few parameters." When you remove parameters, the data becomes more simplified which can weaken the analysis. According to McGregor (2018), statistical power allows the research to detect connections in the data. This would be significant with a population that is too small or too large. If a population is too small, then connections might not be recognizable or could have higher odds of being chance.
Post 2
The main objective of sampling is to extract a representative population from the whole population, as gathering data from each person in a whole population may work against the time allotted for the study and may incur heavy costs (Keller, 2016). A sample size calculator is a tool that is used to determine the size of the sample of a target population. For instance, to carry out a study on a target population that contains 1000 subjects, after applying the sample size calculator, 278 subjects in the population will be required.
Parametric statistics generally require interval or ratio data. An example of this type of data is age, income, height, and weight, in which the intervals between values have meaning, while nonparametric statistics are typically used on data that is nominal or ordinal (Hayes, 2021). Nominal variables are variables for which the values have no quantitative value; for example, sex, race, marital status, educational level, and employment status.
Nonparametric statistic can be used without the mean, sample size, or standard deviation, as is the case with parametric statistics. Moreover, this type of statistical procedure is used when the population data has an unknown distribution or when the sample size is so small that it may be impossible to validate the distribution of the data (Taylor, 2023). Thus, in this case, the application of nonparametric tests is the only suitable option.
Post 3
Statisticians use confidence intervals to measure uncertainty in a sample variable. For example, a researcher selects different samples randomly from the same population and computes a confidence interval for each sample to see how it may represent the true value of the population variable. The resulting datasets are all different where some intervals include the true population parameter and others do not. Thoughts?