Однофакторный дисперсионный анализ (ANOVA)

Use one-way ANOVA when comparing the means of three or more samples defined by a single factor. For example, comparing average fuel consumption across cars from different companies uses one factor—the company. One-way ANOVA cannot simultaneously test multiple factors; if your data involves two factors like company and season, use two-way ANOVA instead.

The null hypothesis states that all sample means are equal. The alternative hypothesis states that the sample means are unequal. These hypotheses are established before analyzing the data. The test determines whether sufficient evidence exists to reject the null hypothesis in favor of the alternative hypothesis.

The F statistic is calculated as the ratio of variance between samples to variance within samples. Variance between samples is the variance of sample means multiplied by sample size. Variance within samples is the average of individual sample variances. This ratio determines whether differences between groups are statistically significant.

An F statistic greater than 1 indicates that variance between samples is larger than variance within samples, producing smaller P-values. This suggests the sample means are unequal, leading to rejection of the null hypothesis. Conversely, F values close to 1 produce larger P-values, suggesting equal sample means and failure to reject the null hypothesis.

One-way ANOVA is designed to analyze samples categorized by only one factor. For example, comparing average mileage of sports bikes by company uses one factor. When data involves two factors—such as company and terrain—the test cannot isolate the effects of each factor independently, requiring two-way ANOVA instead.

One-way ANOVA compares variance between samples to variance within samples. High variance between samples relative to variance within samples produces large F statistics and small P-values, supporting rejection of the null hypothesis. When these variances are similar, the F statistic remains near 1, producing large P-values and supporting the null hypothesis of equal means.

F statistic values far from 1 lead to smaller P-values, indicating significant differences between sample means. F values closer to 1 produce larger P-values, suggesting no significant differences. The P-value determines whether to reject or fail to reject the null hypothesis at a chosen significance level.