0
Absolutely.
In fact I would commonly expect it to be. If, for example, the sample mean for the length of a bolt was 5.5 cm, you would certainly hope the standard deviation was a lot less than 5.5 cm. or it would imply bolts with a negative length (not quite sure how you'd do that without breaching some alternate dimension - no pun intended.)
What else can I help you with?
Is it possible that the standard deviation is less than one?
Yes, a standard deviation can be less than one.
Can a standard deviation of a sample be less than a standard deviation of a population?
Sure it can. But in the survey business, the trick is to select your sample carefully so that they'll be equal, i.e. a sample that is accurately representative of the population.
What happens to the standard deviation as the sample size increases?
As the sample size increases, the standard deviation of the sample mean, also known as the standard error, tends to decrease. This is because larger samples provide more accurate estimates of the population mean, leading to less variability in sample means. However, the standard deviation of the population itself remains unchanged regardless of sample size. Ultimately, a larger sample size results in more reliable statistical inferences.
If the standard deviation is small the data is more dispersed?
No, if the standard deviation is small the data is less dispersed.
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.
Is it possible that the standard deviation is less than one?
Yes, a standard deviation can be less than one.
Can a standard deviation of a sample be less than a standard deviation of a population?
Sure it can. But in the survey business, the trick is to select your sample carefully so that they'll be equal, i.e. a sample that is accurately representative of the population.
How large a sample would be needed to have a standard error less than 2 points for population with a standard deviation of 20?
A sample of size 100.
What happens to the standard deviation as the sample size increases?
As the sample size increases, the standard deviation of the sample mean, also known as the standard error, tends to decrease. This is because larger samples provide more accurate estimates of the population mean, leading to less variability in sample means. However, the standard deviation of the population itself remains unchanged regardless of sample size. Ultimately, a larger sample size results in more reliable statistical inferences.
How would the mean and standard deviation change if the largest data in each set were removed?
Yes. The standard deviation and mean would be less. How much less would depend on the sample size, the distribution that the sample was taken from (parent distribution) and the parameters of the parent distribution. The affect on the sampling distribution of the mean and standard deviation could easily be identified by Monte Carlo simulation.
Does the standard deviation of x decrease in magnitude as the size of the sample gets smaller?
No. But a small sample will be a less accurate predictor of the standard deviation of the population due to its size. Another way of saying this: Small samples have more variability of results, sometimes estimates are too high and other times too low. As the sample size gets larger, there's a better chance that your sample will be close to the actual standard deviation of the population.
Can the mean be less than the standard deviation?
In general, a mean can be greater or less than the standard deviation.
If the standard deviation is small the data is more dispersed?
No, if the standard deviation is small the data is less dispersed.
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.
Is mean deviation less than standard deviation?
Yes, the mean deviation is typically less than or equal to the standard deviation for a given dataset. The mean deviation measures the average absolute deviations from the mean, while the standard deviation takes into account the squared deviations, which can amplify the effect of outliers. Consequently, the standard deviation is usually greater than or equal to the mean deviation, but they can be equal in certain cases, such as when all data points are identical.
What is an acceptable standard deviation?
An acceptable standard deviation depends entirely on the study and person asking for the study. The smaller the standard deviation, the more acceptable it will be because the less likely there are to be errors.
When the population standard deviation is unknown and the sample size is less than 30 what table value should be used in computing a confidence interval for a mean?
t-test for means
