Q: Does outlier affect standard deviation

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On the standard deviation. It has no effect on the IQR.

Yes, any data point outside thestandard deviation its an outlier

Deviation-based outlier detection does not use the statistical test or distance-based measures to identify exceptional objects. Instead, it identifies outliers by examining the main characteristics of objects in a group.

Standard deviation is a measure of the scatter or dispersion of the data. Two sets of data can have the same mean, but different standard deviations. The dataset with the higher standard deviation will generally have values that are more scattered. We generally look at the standard deviation in relation to the mean. If the standard deviation is much smaller than the mean, we may consider that the data has low dipersion. If the standard deviation is much higher than the mean, it may indicate the dataset has high dispersion A second cause is an outlier, a value that is very different from the data. Sometimes it is a mistake. I will give you an example. Suppose I am measuring people's height, and I record all data in meters, except on height which I record in millimeters- 1000 times higher. This may cause an erroneous mean and standard deviation to be calculated.

Information is not sufficient to find mean deviation and standard deviation.

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The mean is "pushed" in the direction of the outlier. The standard deviation increases.

On the standard deviation. It has no effect on the IQR.

The median is least affected by an extreme outlier. Mean and standard deviation ARE affected by extreme outliers.

Outlier does not affect the median.

Yes, any data point outside thestandard deviation its an outlier

The standard deviation is the standard deviation! Its calculation requires no assumption.

Deviation-based outlier detection does not use the statistical test or distance-based measures to identify exceptional objects. Instead, it identifies outliers by examining the main characteristics of objects in a group.

Common method is to find the mean and the standard deviation of the data set and then call anything that falls more than three standard deviations away from the mean an outlier. That is, x is an outlier if abs(x - mean) --------------- > 3 std dev This is usually called a z-test in statistics books, and the ratio abs(x-mean)/(std dev) is abbreviated z. Source: http://mathforum.org/library/drmath/view/52720.html

Standard deviation is a measure of the scatter or dispersion of the data. Two sets of data can have the same mean, but different standard deviations. The dataset with the higher standard deviation will generally have values that are more scattered. We generally look at the standard deviation in relation to the mean. If the standard deviation is much smaller than the mean, we may consider that the data has low dipersion. If the standard deviation is much higher than the mean, it may indicate the dataset has high dispersion A second cause is an outlier, a value that is very different from the data. Sometimes it is a mistake. I will give you an example. Suppose I am measuring people's height, and I record all data in meters, except on height which I record in millimeters- 1000 times higher. This may cause an erroneous mean and standard deviation to be calculated.

The standard deviation of the population. the standard deviation of the population.

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.

This would increase the mean by 6 points but would not change the standard deviation.