A researcher administers an achievement test to assess and indicate th...
Skewness refers to distortion or asymmetry in a symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed. Skewness can be quantified as a representation of the extent to which a given distribution varies from a normal distribution.
Key Points:
Negative Skewness: Negatively skewed distribution refers to the distribution type where more values are plotted on the right side of the graph, where the tail of the distribution is longer on the left side and the mean is lower than the median and mode which it might be zero or negative due to the nature of the data as negatively distributed. Mode > Median > Mean.
For Example, university exams; exams are the same, but a few scores less, a few score average, and a few scores the high percentage, which shows the data is negatively skewed as there is unequal distribution.
An easy test will result in a left-skewed (negatively skewed) distribution of the scores. Thus, the tail of that score distribution will be the lower marks which are on the left-hand side.
This is basically because the frequency of higher scores will be far more than the frequency of low scores, if any, given that the test is an easy one.
Essentially the mode (peak) will be that of a higher score, whereas the median score will be lower than the modal score, and then the mean score which will be the least of all three scores. These are characteristics associated with the left-skewed distribution.
Thus, option A is the correct answer.
In short, easy tests tend to yield negatively skewed score distributions and hard tests tend to yield positively skewed distributions