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Q: What is the shape of a distribution when the mean and the median are about the same value?

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It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.

The mean of a sample is a single value and so its distribution is a single value with probability 1.

for symmetrical distributions your mean equals the median. that is one of the properties of the symmetrical distribution.

i) Since Mean<Median the distribution is negatively skewed ii) Since Mean>Median the distribution is positively skewed iii) Median>Mode the distribution is positively skewed iv) Median<Mode the distribution is negatively skewed

The normal distribution.

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That would provide some evidence that the distribution is symmetric about the mean (or median).

The question is how do the mean and median affect the distribution shape. In a normal curve, the mean and median are both in the same point. ( as is the mode) If a distribution is skewed, its tail is either on the right or the left. If a distribution is skewed the median may be a better value to use than the mean since it has less effect on the shape. Also is there are large outliers, the median has less effect and is better to use. So the mean has a bigger effect on the shape many times than the median.

The Mean is the average of a given set of values. The Median is the value that has the same number of smaller values than the number of higher values, it is in the middle of them. In a symmetrical distribution the Mean is equal to the Median. In an asymmetrical distribution they have different value.

Yes. And in any symmetric distribution, they will.

Mean = average and median = the middle value in an array of all values received. So to answer your question the 50th percentile of a distribution is the same as median.

It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.

No. Normal distribution is uni-modal, specifically with the mean, mode, and median at the same value.

Generally, when the median is greater than the mean it is because the distribution is skewed to the left. This results in outliers or values further below the median than above the median which results in a lower mean value than median value. When a distribution is skewed left, it is generally not very symmetrical or normally distributed.

If it is a symmetric distribution, the median must be 130.

The mean of a sample is a single value and so its distribution is a single value with probability 1.

Either can be used for symmetrical distributions. For skewed data, the median may be more a appropriate measure of the central tendency - the "average".

When someone asks a for an "average" value, that can mean a couple of different things. "Mean," "median," and "mode" are all values that are used to relate what the "center" or "average" of a distribution of values is. Each one has their advantages and disadvantages. The median is the value that divides the distribution exactly into halves - 50% is below it, and 50% above it. The median may not actually occur in the distribution, but it is the "balance point" of the distribution. The main advantage of the median is that it is not affected by outliers as the mean is and the mode can be. In distributions with a clear skew, such as housing prices or wages, using the median provides a much better estimate of what the "average" is.

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