Can you compare standard deviations with different means?
In many experimental contexts, the finding of different standard deviations is as important as the finding of different means. If the standard deviations are different, then the populations are different regardless of what the t test concludes about differences between the means.
How do you check equality of standard deviations?
An F-test ( Snedecor and Cochran, 1983) is used to test if the standard deviations of two populations are equal. This test can be a two-tailed test or a one-tailed test. The two-tailed version tests against the alternative that the standard deviations are not equal.
Why do we test for differences in standard deviation?
Because the standard deviation is the square root of the variance, a hypothesis test that compares standard deviations is equivalent to a hypothesis test that compares variances. Many statistical methods have been developed to compare the variances from two or more populations.
How do you compare two variables with standard deviation?
Step 1) Calculate the mean (average) of the variable, Step 2) Subtract mean from each of the observation and square it, Step 3) Sum up the values obtained from Step 2, Step 4) Divide the value obtained in Step 3 by the number of observations.
Is it possible to create two data sets with similar standard deviations but different means provide your example?
Two data sets can have the same mean and Standard Deviation but have different values of data points. For example if one data set is a linear multiple of the other, or a mirror of the other with some random noise, they could have same mean and Standard Deviation.
What distribution do you use to test to see if two standard deviations are equal?
An F-test (Snedecor and Cochran, 1983) is used to test if the variances of two populations are equal.
How do you know if population standard deviation is known unknown?
Population Standard Deviation Unknown If the population standard deviation, sigma is unknown, then the mean has a student’s t (t) distribution and the sample standard deviation is used instead of the population standard deviation.
What does it mean when two standard deviations are similar?
This implies they have the same quadratic deviation from the mean AKA Variance.
How do you know if ap value is significant?
If the p-value is 0.05 or lower, the result is trumpeted as significant, but if it is higher than 0.05, the result is non-significant and tends to be passed over in silence.
Can two data sets have the same mean and standard deviations?