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.
If the standard deviation of 10 scores is zero, then all scores are the same.
5.142857143 is the mean.12.43956044 is the variance.3.526976104 is the standard deviation.
Since the standard deviation is zero, the scores are all the same. And, since their mean is 10, they must all be 10.
Variance = 17.9047619 Standard Deviation = 4.23140188
[10, 10, 10, 10, 10, 10, 10] has a mean of 10 and a standard deviation of zero.
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.
6.3
If the standard deviation of 10 scores is zero, then all scores are the same.
5.142857143 is the mean.12.43956044 is the variance.3.526976104 is the standard deviation.
The formula for the standard deviation of a sample (s) is given by: s =√(⅟₍n₌₁₎Σ(y-ȳ)²) where y are the data points and ȳ is their mean; it can be rearranged to give: s = √(⅟₍n₌₁₎(Σy² - n((Σy)/n)²) → s = √(⅟₍₅₌₁₎(1815 - 5(⁹⁵/₅)²) → s = √(¼ × 10) → s = √2.5
Mean: 26.33 Median: 29.5 Mode: 10, 35 Standard Deviation: 14.1515 Standard Error: 5.7773
Since the standard deviation is zero, the scores are all the same. And, since their mean is 10, they must all be 10.
Standard Deviation = (principal value of) the square root of Variance. So SD = 10.
Variance = 17.9047619 Standard Deviation = 4.23140188
The deviation is 1694.
It is 10.