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.
No.
The more precise a result, the smaller will be the standard deviation of the data the result is based upon.
It is inversely proportional; a larger standard deviation produces a small kurtosis (smaller peak, more spread out data) and a smaller standard deviation produces a larger kurtosis (larger peak, data more centrally located).
If I take 10 items (a small sample) from a population and calculate the standard deviation, then I take 100 items (larger sample), and calculate the standard deviation, how will my statistics change? The smaller sample could have a higher, lower or about equal the standard deviation of the larger sample. It's also possible that the smaller sample could be, by chance, closer to the standard deviation of the population. However, A properly taken larger sample will, in general, be a more reliable estimate of the standard deviation of the population than a smaller one. There are mathematical equations to show this, that in the long run, larger samples provide better estimates. This is generally but not always true. If your population is changing as you are collecting data, then a very large sample may not be representative as it takes time to collect.
The smaller the standard deviation, the closer together the data is. A standard deviation of 0 tells you that every number is the same.
Standard deviation measures the amount of variation or dispersion in a dataset. It quantifies how much individual data points deviate from the mean of the dataset. A larger standard deviation indicates that data points are spread out over a wider range of values, while a smaller standard deviation suggests that they are closer to the mean. Thus, the standard deviation is directly influenced by the values and distribution of the data points.
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.
No.
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.
The more precise a result, the smaller will be the standard deviation of the data the result is based upon.
In the same way that you calculate mean and median that are greater than the 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.
The absolute value of the standard score becomes smaller.
No.
Yes. If the variance is less than 1, the standard deviation will be greater that the variance. For example, if the variance is 0.5, the standard deviation is sqrt(0.5) or 0.707.
Yes, that's true. In a normal distribution, a smaller standard deviation indicates that the data points are closer to the mean, resulting in a taller and narrower curve. Conversely, a larger standard deviation leads to a wider and shorter curve, reflecting more variability in the data. Thus, the standard deviation directly affects the shape of the normal distribution graph.