College of DuPage Parametric and Non Parametric Statistics Discussion

Question Description

Respond to the following in a minimum of 100 words each:

From my reading of the text in (Jackson, 2017), parametric statistics depend upon a normal curve’s parameters. Since parametric measurements depends on whether the curve is normal, information must meet certain presumptions, or parametric insights cannot be determined. Preceding running any parametric measurements, one must consistently make certain to test the presumptions for the tests that you are intending to run. In addition to parametric, there is non-parametric statistics. Non-parametric statistics have a normal curve that is not founded on parameters In this manner, if your information damage the assumptions of a normal parametric and non-parametric measurements may better characterize the information, one should attempt to run the non-parametric equivalent of the parametric analysis.

According to (Jackson, 2017), “the chi-square (x2) goodness-of-fit test is used for comparing categorical information against what we would expect based on previous knowledge. As such, it tests what are called observed frequencies (the frequency with which participants fall into a category) against expected frequencies (the frequency expected in a category if the sample data represent the population). It is a non-directional test, meaning that the alternative hypothesis is neither one-tailed nor two-tailed” (p.355).

Use the chi-square test of goodness-of-fit when you have one nominal variable with two or more values (such as red, pink, and white flowers). You compare the observed counts of observations in each category with the expected counts, which you calculate using theoretical expectation.

Reference:

Jackson, S.L. (2017). Statistics plain and simple (4^th ed.). Boston, MA: Cengage.

Numerous fundamental statistical theories are beneficial prerequisite information for fully comprehension the words “parametric” and “nonparametric.” These statistical essentials consist of random variables, probability distributions, parameters, population, sample, sampling distributions along with the Central Limit Theorem. I cannot describe these subjects within a couple of paragraphs, as per they should normally encompass two or three chapters in our statistics eBooks. Therefore, I will limit my description to some supportive (I hope) links between phrases. The subject of statistics occurs because it’s normally impossible to accumulate information from each persons of concern (population). As said by Walsh (1962).” we as student only answer is to gather material from a subset (example) of the persons of curiosity, then again undergraduates real demand is to distinguish the truth regarding the population.” (p. 2). Quantities for instance, means, standard deviations and percentages are all imperative values and are known as parameters whenever student and professors are speaking about a population. And, we normally cannot obtain info from the entire population, students cannot identify the meanings regarding the parameters pertain to that population. But, students can, still, compute estimates of each quantities for their sample.

References:

Hoskin,Tanya https://www.mayo.edu/research/documents/parametric-and-nonparametric-demystifying-the-terms/doc-20408960

Walsh, J.E. (1962). Retrieved from https://www.mayo.edu/research/documents/parametric-and-nonparametric-demystifying-the-terms/doc-20408960Handbook of Nonparametric Statistics, New York: D.V. Nostrand.