Search results
Results from the WOW.Com Content Network
In statistics, an F-test of equality of variances is a test for the null hypothesis that two normal populations have the same variance.Notionally, any F-test can be regarded as a comparison of two variances, but the specific case being discussed in this article is that of two populations, where the test statistic used is the ratio of two sample variances. [1]
In statistics, one-way analysis of variance (or one-way ANOVA) is a technique to compare whether two or more samples' means are significantly different (using the F distribution). This analysis of variance technique requires a numeric response variable "Y" and a single explanatory variable "X", hence "one-way". [1]
In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant .
If one's F-statistic is greater in magnitude than their critical value, we can say there is statistical significance at the 0.05 alpha level. The F-test is used for comparing the factors of the total deviation. For example, in one-way, or single-factor ANOVA, statistical significance is tested for by comparing the F test statistic
The difference between the two sample means, each denoted by X i, which appears in the numerator for all the two-sample testing approaches discussed above, is ¯ ¯ = The sample standard deviations for the two samples are approximately 0.05 and 0.11, respectively. For such small samples, a test of equality between the two population variances ...
In statistics, the two-way analysis of variance (ANOVA) is an extension of the one-way ANOVA that examines the influence of two different categorical independent variables on one continuous dependent variable. The two-way ANOVA not only aims at assessing the main effect of each independent variable but also if there is any interaction between them.
Statistical testing uses data from samples to assess, or make inferences about, a statistical population.For example, we may measure the yields of samples of two varieties of a crop, and use a two sample test to assess whether the mean values of this yield differs between varieties.
The 4 values in red are drawn from one distribution, and the 5 values in blue from another; we'd like to test whether the mean values of the two distributions are different. The hypothesis is that the mean of the first distribution is higher than the mean of the second; the null hypothesis is that both groups of samples are drawn from the same ...