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Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.

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Q: When computing Standard deviation should you eliminate the extreme values?
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Why is the standard deviation of a distribution of means smaller than the standard deviation of the population from which it was derived?

The reason the standard deviation of a distribution of means is smaller than the standard deviation of the population from which it was derived is actually quite logical. Keep in mind that standard deviation is the square root of variance. Variance is quite simply an expression of the variation among values in the population. Each of the means within the distribution of means is comprised of a sample of values taken randomly from the population. While it is possible for a random sample of multiple values to have come from one extreme or the other of the population distribution, it is unlikely. Generally, each sample will consist of some values on the lower end of the distribution, some from the higher end, and most from near the middle. In most cases, the values (both extremes and middle values) within each sample will balance out and average out to somewhere toward the middle of the population distribution. So the mean of each sample is likely to be close to the mean of the population and unlikely to be extreme in either direction. Because the majority of the means in a distribution of means will fall closer to the population mean than many of the individual values in the population, there is less variation among the distribution of means than among individual values in the population from which it was derived. Because there is less variation, the variance is lower, and thus, the square root of the variance - the standard deviation of the distribution of means - is less than the standard deviation of the population from which it was derived.


Why does the standard deviation weight extreme deviations more than does the arithmetic mean?

Because the standard deviation is based on the square root of the sum of the squares of the deviations, and, as a result, the sum of the squares of the deviations puts more weight in outliers than does a simple arithmetic mean.Note: I wrote this and then had second thoughts, but I'm keeping it in so that someone with more knowledge can weigh in (pun intended). I'm not certain how the arithmetic mean factors into the question. I think the questioner, and definitely this answerer, is confused.


Score that is 15 above the means be an extreme score?

It depends entirely on the variance (or standard error).


What is a z-score used for?

The z-score is used to convert a variable with a Gaussian [Normal] distribution with mean m and standard error s to a variable with a standard normal distribution. Since the latter is tabulated, the probability of an outcome as extreme or more compared to the one observed is easily obtained.


Is there any distinction between an extreme point and an extreme value of a function?

no

Related questions

Which of the following is least affected if an extreme high outlier is added to your data mean median or standard deviation or ALL?

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


What is a gross deviation from a standard of reasonable care?

extreme lack of attention to medical care


The project sponsored by the United Nations to eliminate extreme poverty worldwide is called the?

The project sponsored by the United Nations to eliminate extreme poverty worldwide is called the Millenium Project.


Why is the standard deviation of a distribution of means smaller than the standard deviation of the population from which it was derived?

The reason the standard deviation of a distribution of means is smaller than the standard deviation of the population from which it was derived is actually quite logical. Keep in mind that standard deviation is the square root of variance. Variance is quite simply an expression of the variation among values in the population. Each of the means within the distribution of means is comprised of a sample of values taken randomly from the population. While it is possible for a random sample of multiple values to have come from one extreme or the other of the population distribution, it is unlikely. Generally, each sample will consist of some values on the lower end of the distribution, some from the higher end, and most from near the middle. In most cases, the values (both extremes and middle values) within each sample will balance out and average out to somewhere toward the middle of the population distribution. So the mean of each sample is likely to be close to the mean of the population and unlikely to be extreme in either direction. Because the majority of the means in a distribution of means will fall closer to the population mean than many of the individual values in the population, there is less variation among the distribution of means than among individual values in the population from which it was derived. Because there is less variation, the variance is lower, and thus, the square root of the variance - the standard deviation of the distribution of means - is less than the standard deviation of the population from which it was derived.


What is the full form of xps?

In regards to Dell Computing it stands for eXtreme Performance System.


What kind of natural Selection would eliminate one extreme?

Directional selection


What would -2 standard deviation below the mean be?

It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.


Why does the standard deviation weight extreme deviations more than does the arithmetic mean?

Because the standard deviation is based on the square root of the sum of the squares of the deviations, and, as a result, the sum of the squares of the deviations puts more weight in outliers than does a simple arithmetic mean.Note: I wrote this and then had second thoughts, but I'm keeping it in so that someone with more knowledge can weigh in (pun intended). I'm not certain how the arithmetic mean factors into the question. I think the questioner, and definitely this answerer, is confused.


When to use quartile deviation?

When you are looking for a simple measure of the spread of the data, but one which is protected from the effects of extreme values (outliers).


Directional selection tends to eliminate?

organisms on one extreme of the population have a better chance to survive than do those on the other extreme.


Steps for how to do a z-score problem in statistics?

If you have a variable X distributed with mean m and standard deviation s, then the z-score is (x - m)/s. If X is normally distributed, or is the mean of a random sample then Z has a Standard Normal distribution: that is, a Gaussian distribution with mean 0 and variance 1. The probability density function of Z is tabulated so that you can check the probability of observing a value as much or more extreme.


Where is yuma AZ in mt standard time zone?

On the extreme western edge of Arizona