Q: What is the relationship of the standard error of the mean to the standard error of the difference?

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Mean: 26.33 Median: 29.5 Mode: 10, 35 Standard Deviation: 14.1515 Standard Error: 5.7773

Standard error (statistics)From Wikipedia, the free encyclopediaFor a value that is sampled with an unbiased normally distributed error, the above depicts the proportion of samples that would fall between 0, 1, 2, and 3 standard deviations above and below the actual value.The standard error is a method of measurement or estimation of the standard deviation of the sampling distribution associated with the estimation method.[1] The term may also be used to refer to an estimate of that standard deviation, derived from a particular sample used to compute the estimate.For example, the sample mean is the usual estimator of a population mean. However, different samples drawn from that same population would in general have different values of the sample mean. The standard error of the mean (i.e., of using the sample mean as a method of estimating the population mean) is the standard deviation of those sample means over all possible samples (of a given size) drawn from the population. Secondly, the standard error of the mean can refer to an estimate of that standard deviation, computed from the sample of data being analyzed at the time.A way for remembering the term standard error is that, as long as the estimator is unbiased, the standard deviation of the error (the difference between the estimate and the true value) is the same as the standard deviation of the estimates themselves; this is true since the standard deviation of the difference between the random variable and its expected value is equal to the standard deviation of a random variable itself.In practical applications, the true value of the standard deviation (of the error) is usually unknown. As a result, the term standard error is often used to refer to an estimate of this unknown quantity. In such cases it is important to be clear about what has been done and to attempt to take proper account of the fact that the standard error is only an estimate. Unfortunately, this is not often possible and it may then be better to use an approach that avoids using a standard error, for example by using maximum likelihood or a more formal approach to deriving confidence intervals. One well-known case where a proper allowance can be made arises where Student's t-distribution is used to provide a confidence interval for an estimated mean or difference of means. In other cases, the standard error may usefully be used to provide an indication of the size of the uncertainty, but its formal or semi-formal use to provide confidence intervals or tests should be avoided unless the sample size is at least moderately large. Here "large enough" would depend on the particular quantities being analyzed (see power).In regression analysis, the term "standard error" is also used in the phrase standard error of the regression to mean the ordinary least squares estimate of the standard deviation of the underlying errors.[2][3]

From what ive gathered standard error is how relative to the population some data is, such as how relative an answer is to men or to women. The lower the standard error the more meaningful to the population the data is. Standard deviation is how different sets of data vary between each other, sort of like the mean. * * * * * Not true! Standard deviation is a property of the whole population or distribution. Standard error applies to a sample taken from the population and is an estimate for the standard deviation.

(15/sqroot(9))=5 So it is 5

This question requires care to prevent confusion, and a basic knowledge of statistics. I've seen three types of descriptive statistical error bar used: standard deviation, standard error, or confidence interval. The use of any of these indicates that each point in the graph around which error bars are placed is the mean of a set of values. The error bars then give an indication of this set of data: * Standard deviation gives an indication of the variability of the underlying set of values. * Standard error gives an indication of how close the calculated mean of the set of values is to the mean of the entire population of these values (this is dependant on the number of these values the mean is found from - the greater the number of values used, the smaller the standard error). * The condfidence interval is the range which is likely to contain the true population mean (and is thus related to the standard error). Each of these may be used depending on the data used. However, you have to be careful since the first two of these are often confused - and the type of error bar used is often not labelled at all. Hope that helped :-)

Related questions

Standard error is random error, represented by a standard deviation. Sampling error is systematic error, represented by a bias in the mean.

standard error

Standard error of the mean (SEM) and standard deviation of the mean is the same thing. However, standard deviation is not the same as the SEM. To obtain SEM from the standard deviation, divide the standard deviation by the square root of the sample size.

Variance, standard deviation and standard error are the most common but there are also mean absolute error, standardised error range inter-quartile range The use of "error" does not mean that anything is wrong - the expression simply means difference from the expected value.

The standard error increases.

the purpose and function of standard error of mean

The standard error of the underlying distribution, the method of selecting the sample from which the mean is derived, the size of the sample.

Your question is asking for a number to be divided by itself. Can you clarify your problem.

An estimate for the mean of a set of observations is just that - an estimate. Another set of observations will give a different estimates. These estimates for the mean will have a distribution which will have a standard error. If you have two sub-populations, the mean of each sub-population will have a standards error and the se of the difference between the means is a measure of the variability of the estimates of the difference.A typical school work example: the heights of men and of women. There will be a mean height for men, Hm, with a se for men's heights and a mean height for women, Hw, with its own se. The difference in mean heights is Hm - Hw and which will have an estimated se.

yes

Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.

Mean: 26.33 Median: 29.5 Mode: 10, 35 Standard Deviation: 14.1515 Standard Error: 5.7773