What happens when a distribution is positively skewed?
A. Standard deviation overestimates risk B. Standard deviation correctly estimates risk C. Standard deviation underestimates risk D. The tails are fatter than in a normal distribution E. None of the above
The correct answer to this question is A, and Standard deviation overestimates risk. This is related to statistics, and it is a measurement. This measurement brings quantity to several variations in a set of data values. Low standard deviations mean many of the numbers are close to the average.
A high standard deviation means the numbers are not close together. Another use of standard deviation is to measure how close a number is to bring the right number. Another way to describe it is the margin of error and this is popular in polls, especially political and household polls.
When a distribution is positively skewed it is very different from a typical bell curve result, a normal distribution, as it has a long right-hand tail. High scores are occurring at the extreme, so the distribution will have the mean score to the right of the peak.
The mean is usually greater than the median, which is always greater than the mode (most displayed score). If a group's income was shown in a distribution it would likely follow a standard distribution, but if there were two or three millionaires, their scores would skew the resulting distribution positively. In a risk calculation, a positive skew would indicate greater risks to be considered.
There are two kinds of skewed distributions. A distribution is categorically skewed if the totals fall toward the lower side of the scale and there are very few higher results. Positively skewed data is also known as skewed to the right because that is the direction of the long tail end of the chart.
In probability theory and statistics, skewness is a gauge of the asymmetry of the likelihood distribution of a real-valued arbitrary variable about its mean. The variable about it’s mean. The skewness worth can be positive or negative, or undefined.