Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.
Pooled variance is a method for estimating variance given several different samples taken in different circumstances where the mean may vary between samples but the true variance (equivalently, precision) is assumed to remain the same. A combined variance is a method for estimating variance from several samples, given the size, mean and standard deviation of each. Mathematically, a combined variance is equal to the calculated variance of the set of the data from all samples. See links.
Both are parametric test. The t-test uses a test statistic that is related to the sample mean(s) and is used to compare that with the mean of another sample or some population. The F-test uses a test statistic that is related to the sample variance and is used to compare that with the variance of another sample or some population. Both tests require identical independently distributed random variables. This ensures that the relevant test statistics are approximately normally distributed.
Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!
A t-test is a inferential statistic. Other inferential statistics are confidence interval, margin of error, and ANOVA. An inferential statistic infers something about a population. A descriptive statistic describes a population. Descriptive statistics include percentages, means, variance, and regression.
Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.
Pooled variance is a method for estimating variance given several different samples taken in different circumstances where the mean may vary between samples but the true variance (equivalently, precision) is assumed to remain the same. A combined variance is a method for estimating variance from several samples, given the size, mean and standard deviation of each. Mathematically, a combined variance is equal to the calculated variance of the set of the data from all samples. See links.
Both are parametric test. The t-test uses a test statistic that is related to the sample mean(s) and is used to compare that with the mean of another sample or some population. The F-test uses a test statistic that is related to the sample variance and is used to compare that with the variance of another sample or some population. Both tests require identical independently distributed random variables. This ensures that the relevant test statistics are approximately normally distributed.
If you know the population variance or if you have a very large sample then you could reliably use a Z test. Otherwise you should use a t test and use s^2 as an estimator for the population variance.
Equal Variance
Equal Variance
The Fisher F-test for Analysis of Variance (ANOVA).
Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!
William C. Guenther has written: 'A sample size formula for the hypergeometric' -- subject(s): Hypergeometric distribution, Sampling (Statistics) 'Concepts of probability' -- subject(s): Probabilities 'A sample size formula for a non-central t test' -- subject(s): Sampling (Statistics), Statistical hypothesis testing, T-test (Statistics) 'Analysis of variance' -- subject(s): Analysis of variance
A t-test is a inferential statistic. Other inferential statistics are confidence interval, margin of error, and ANOVA. An inferential statistic infers something about a population. A descriptive statistic describes a population. Descriptive statistics include percentages, means, variance, and regression.
When data is homogeneous over k independent samples of size n_i for i=1,2,...,k, the pooled variance is given by s_p^2=((n_1-1) s_1^2+(n_2-1) s_2^2+⋯(n_k-1) s_k^2)/(n_1+n_2+⋯+n_k-k)
Yes.Yes.Yes.Yes.