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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.
What else can I help you with?
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 does three standard deviation mean?
Three standard deviations refer to a statistical measure that indicates the range within which approximately 99.7% of data points in a normal distribution fall. In other words, if you have a dataset with a mean (average) value and a standard deviation, three standard deviations above and below the mean encompass nearly all the data points, highlighting the spread and variability of the data. This concept is often used in quality control and statistics to identify outliers or extreme values.
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.
Why range is considered as good measure of variability and why standard deviation is preferred over the other?
Range is considered a good measure of variability because it provides a simple and quick assessment of the spread of data by capturing the difference between the maximum and minimum values. However, it is sensitive to outliers and does not account for the distribution of values between the extremes. Standard deviation is preferred because it considers how each data point deviates from the mean, providing a more comprehensive view of variability, and it is less influenced by extreme values. This makes standard deviation a more robust and informative measure for understanding the dispersion of data.
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.
What does three standard deviation mean?
Three standard deviations refer to a statistical measure that indicates the range within which approximately 99.7% of data points in a normal distribution fall. In other words, if you have a dataset with a mean (average) value and a standard deviation, three standard deviations above and below the mean encompass nearly all the data points, highlighting the spread and variability of the data. This concept is often used in quality control and statistics to identify outliers or extreme values.
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
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".
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.
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.
Why range is considered as good measure of variability and why standard deviation is preferred over the other?
Range is considered a good measure of variability because it provides a simple and quick assessment of the spread of data by capturing the difference between the maximum and minimum values. However, it is sensitive to outliers and does not account for the distribution of values between the extremes. Standard deviation is preferred because it considers how each data point deviates from the mean, providing a more comprehensive view of variability, and it is less influenced by extreme values. This makes standard deviation a more robust and informative measure for understanding the dispersion of data.
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.
