Distributions A and B have the same mean and range. The standard deviations of distribution A and distribution B are 15 and 5, respectively. We may conclude that: the scores in distribution B are grouped closer to the mean than the scores in distribution A. the scores in distribution A are grouped closer to the mean than the scores in distribution B. there are three times as many scores from −1 standard deviation to +1 standard deviation in distribution A. there is one-third as many scores from −1 standard deviation to +1 standard deviation in distribution A. We cannot conclude anything unless we know the value of the mean.
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
Distributions A and B have the same
- the scores in distribution B are grouped closer to the mean than the scores in distribution A.
- the scores in distribution A are grouped closer to the mean than the scores in distribution B.
- there are three times as many scores from −1 standard deviation to +1 standard deviation in distribution A.
- there is one-third as many scores from −1 standard deviation to +1 standard deviation in distribution A.
- We cannot conclude anything unless we know the value of the mean.
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