It simply means that you have a sample with a smaller variation than the population itself. In the case of random sample, it is possible.
No. The standard deviation is not exactly a value but rather how far a score deviates from the mean.
There's no valid answer to your question. The problem is a standard deviation can be close to zero, but there is no upper limit. So, I can make a statement that if my standard deviation is much smaller than my mean, this indicates a low standard deviation. This is somewhat subjective. But I can't make say that if my standard deviation is many times the mean value, that would be considered high. It depends on the problem at hand.
No standard deviation can not be bigger than maximum and minimum values.
No. The expected value is the mean!
Standard Deviation = (principal value of) the square root of Variance. So SD = 10.
Let sigma = standard deviation. Standard error (of the sample mean) = sigma / square root of (n), where n is the sample size. Since you are dividing the standard deviation by a positive number greater than 1, the standard error is always smaller than the standard deviation.
The error, which can be measured in a number of different ways. Error, percentage error, mean absolute deviation, standardised error, standard deviation, variance are some measures that can be used.
The absolute value of the standard score becomes smaller.
Standard deviation in statistics refers to how much deviation there is from the average or mean value. Sample deviation refers to the data that was collected from a smaller pool than the population.
A small sample and a large standard deviation
The answer depends on the value of the new point. If the new value is near the mean then the new standard deviation (SD) will be smaller, if it is far away, the new SD will be larger.
The sample standard deviation (s) divided by the square root of the number of observations in the sample (n).
In statistical analysis, the value of sigma () can be determined by calculating the standard deviation of a set of data points. The standard deviation measures the dispersion or spread of the data around the mean. A smaller standard deviation indicates that the data points are closer to the mean, while a larger standard deviation indicates greater variability. Sigma is often used to represent the standard deviation in statistical formulas and calculations.
The standard error of the mean and sampling error are two similar but still very different things. In order to find some statistical information about a group that is extremely large, you are often only able to look into a small group called a sample. In order to gain some insight into the reliability of your sample, you have to look at its standard deviation. Standard deviation in general tells you spread out or variable your data is. If you have a low standard deviation, that means your data is very close together with little variability. The standard deviation of the mean is calculated by dividing the standard deviation of the sample by the square root of the number of things in the sample. What this essentially tells you is how certain are that your sample accurately describes the entire group. A low standard error of the mean implies a very high accuracy. While the standard error of the mean just gives a sense for how far you are away from a true value, the sampling error gives you the exact value of the error by subtracting the value calculated for the sample from the value for the entire group. However, since it is often hard to find a value for an entire large group, this exact calculation is often impossible, while the standard error of the mean can always be found.
The mean is the average value and the standard deviation is the variation from the mean value.
No. The standard deviation is not exactly a value but rather how far a score deviates from the mean.
There's no valid answer to your question. The problem is a standard deviation can be close to zero, but there is no upper limit. So, I can make a statement that if my standard deviation is much smaller than my mean, this indicates a low standard deviation. This is somewhat subjective. But I can't make say that if my standard deviation is many times the mean value, that would be considered high. It depends on the problem at hand.