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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.
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When computing Standard deviation should you eliminate the extreme values?
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.
Importance of measures of central tendency?
Measures of the general value are a common need. Average, Median, and Mode are the three commonest.Average is the arithmetic average of all the values.Median is the actual measurement which is midwaybetween the extreme values, and is often closest to the average.Mode is the commonest value.Other indicators of central tendency, may ignore all value beyond say, three standard deviations, and thus ignore the contribution by the extreme, and uncommon, values.
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.
Score that is 15 above the means be an extreme score?
It depends entirely on the variance (or standard error).
Advantages and disadvantages of using arithmetic mean?
"The advantage is that the mean takes every value into account. A disadvantage is that it can be affected by extreme values. " The mean or more properly the "arithmetic mean" of a sample will eventually approximate the mean of the distribution of the population as the sample size increases. If the population distribution is skewed (not symmetrical), the mode and median will not provide an estimate of the mean, even as the sample size becomes large.
When computing Standard deviation should you eliminate the extreme values?
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.
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.
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
What is measurement that fall beyond three standard deviations from the mean?
It is a measurement which may, sometimes, be called an extreme observation or an outlier. However, there is no agreed definition for outliers.
Measurements that fall beyond three standard deviations from the mean are called?
Extreme values. They might also be called outliers but there is no agreed definition for the term "outlier".
Importance of measures of central tendency?
Measures of the general value are a common need. Average, Median, and Mode are the three commonest.Average is the arithmetic average of all the values.Median is the actual measurement which is midwaybetween the extreme values, and is often closest to the average.Mode is the commonest value.Other indicators of central tendency, may ignore all value beyond say, three standard deviations, and thus ignore the contribution by the extreme, and uncommon, values.
What are the properties of arithmetic mean?
The arithmetic mean, also known as the average, is calculated by adding up all the values in a dataset and then dividing by the total number of values. It is a measure of central tendency that is sensitive to extreme values, making it less robust than the median. The arithmetic mean follows the properties of linearity, meaning that it can be distributed across sums and differences in a dataset. Additionally, the sum of the deviations of each data point from the mean is always zero.
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.
Advantages and disadvantages of mean deviation?
The mean deviation is a measure of dispersion that calculates the average absolute difference between each data point and the mean. One advantage of mean deviation is that it considers every data point in the calculation, providing a more balanced representation of the data spread. However, a disadvantage is that it can be sensitive to outliers, as it does not square the differences like the variance does in standard deviation, making it less robust in the presence of extreme values.
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).
What is affected by extreme outliers?
Extreme outliers can greatly distort statistical measures such as the mean and standard deviation, making them less representative of the data. They can also impact the accuracy of predictive models by leading to overfitting. In some cases, outliers may signal data quality issues or the presence of unexpected patterns in the data that warrant further investigation.
