Why might nonparametric statistical methods be used for analysis? ...

[ghost!]

Why might nonparametric statistical methods be used for analysis?
 
  a. The level of measurement of the variables is nominal.
  b. The researcher prefers to use these statistical tests.
  c. The null hypothesis is absent.
  d. The sample is small or lacks a normal distribution.

Question 2

Which of the following are true in a skewed distribution? (Select all that apply.)
 
  a. Mean, mode, and median are not equal.
  b. The curve is asymmetrical.
  c. There is bimodal distribution.
  d. There is no mode.
  e. More than 50 of the values lie to one side of the mean.

[joshraies]

Answer to Question 1

ANS: A, D
The most commonly used type of statistical analysis is parametric statistics. The analysis is referred to as parametric statistical analysis because the findings are inferred to the parameters of a normally distributed population. These approaches to analysis require meeting the following three assumptions before they can justifiably be used: (1) normal distribution and variance can be calculated, (2) level of measurement at least interval, or ordinal with an approximately normal distribution, and (3) data that can be treated as a random sample. Nonparametric statistical analysis, or distribution-free techniques, can be used in studies that do not meet the first two assumptions of normal distribution and at least interval level data.

Answer to Question 2

ANS: A, B, E
Any curve that is not symmetrical is referred to as skewed or asymmetrical. A curve may be positively skewed, which means that the largest portion of data is below the mean. A curve can also be negatively skewed, which means that the largest portion of data is above the mean. In a skewed distribution, the mean, median, and mode are not equal. In a positively skewed distribution, the mean is greater than the median, which is greater than the mode. In a negatively skewed distribution, the mean is less than the median, which is less than the mode.