Yes. If you have very high or very low outliers in your data set, it is generally preferred to use the median - the mid-point when all data points are arranged from least to greatest.
A good example for when to avoid the mean and prefer the median is salary. The mean is less good here as there are a few very high salaries which skew the distribution to the right. This drags the mean higher to the point where it is disproportionately affected by the few higher salaries. In this case, the median would only be slightly affected by the few high salaries and is a better representation of the whole of the data.
In general, if the distribution is not normal, the mean is less appropriate than the median.
An outlier does affect the mean of the data. How it's affected depends on how many data points there are, how far from the data the outlier is, whether it is greater than the mean (increases mean) or less than the mean (decreases the mean).
An outlier will pull the mean and median towards itself. The extent to which the mean is affected will depend on the number of observations as well as the magnitude of the outlier. The median will change by a half-step.
The median is least affected by an extreme outlier. Mean and standard deviation ARE affected by extreme outliers.
The mean is "pushed" in the direction of the outlier. The standard deviation increases.
An outlier can increase or decrease the mean and median It usually doesn't affect the mode
An outlier does affect the mean of the data. How it's affected depends on how many data points there are, how far from the data the outlier is, whether it is greater than the mean (increases mean) or less than the mean (decreases the mean).
Calculate the mean, median, and range with the outlier, and then again without the outlier. Then find the difference. Mode will be unaffected by an outlier.
An outlier pulls the mean towards it. It does not affect the median and only affects the mode if the mode is itself the outlier.
Yes, it will. An outlier is a data point that lies outside the normal range of data. This means that if it is factored in the mean will move in the direction the outlier is, really high if the outlier was high, and really low if the outlier was low.
It will go down; with outlier, mean is 42 without outlier, mean is 32.5
The mean is changed.
it messes up the mean and sometimes the median. * * * * * An outlier cannot mess up the median.
An outlier will pull the mean and median towards itself. The extent to which the mean is affected will depend on the number of observations as well as the magnitude of the outlier. The median will change by a half-step.
An outlier looks like a piece of data that does not fit the pattern of most of the data. However just because some data point "looks like an outlier" does not necessarily mean that it is - standards for deciding whether something is an outlier or not varies a lot from course to course (and how accurate you want to be), so one person's outlier is another persons normal data.
By definition, an outlier will not have the same value as other data points in the dataset. So, the correct question is "What is the effect of an outlier on a dataset's mean." The answer is that the outlier moves the mean away from the value of the other 49 identical values. If the outlier is the "high tail" the mean is moved to a higher value. If the outlier is a "low tail" the mean is moved to a lower value.
The outlier is capable of affecting mean median mode and range it affects mean because the average has changed if affects median because you have to cross out 1 more letter it doesn't affect mode it does affect range because an outlier is a number that i far away from the other numbers * * * * * It does not affect the median.
Such a data point is called an outlier.