The phrase "analysis of variance tests" is correct and usable in written English.
It is typically used in statistical contexts to refer to a set of statistical methods used to compare means among different groups.
Example: "The analysis of variance tests revealed significant differences between the treatment groups."
Alternatives: "ANOVA tests" or "variance analysis methods".
The analysis of variance tests is a usual statistical method of the different fields.
Analysis of variance tests were conducted to evaluate the statistical suitability of the models developed.
Statistical analyses were performed by use of factorial dependent-measures analysis of variance tests.
P-values were calculated by two-sided Student's T-test or by analysis of variance tests (ANOVA) unless indicated otherwise.
Finally, analysis of variance tests (ANOVA) and correlations were used to test the effects on continuous variables.
One-way Analysis of Variance tests (ANOVA) and t-tests were used to compare groups of respondents in terms of mean satisfaction ratings, achievement ratings and factor scores.
Analysis of variance tests indicated that the number of extraction steps and the IL/gasoline volume ratio were of statistically highly significant, but none of the interactions were significant.
Factorial analysis of variance tests has suggested that the performance of the aforementioned algorithms is sensitive to several degradation sources including background noise level, noise type, and codec-PLC type.
The first method, based on analysis of variance tests, investigates the performance sensitivity of current state-of-the-art algorithms to different wireless-VoIP degradation sources, such as noise type, noise level, packet losses, and codec-PLC type.
Likewise, separate Friedman two-way analysis of variance tests followed by multiple pairwise comparisons when appropriate were performed to determine if the neutrophil and lymphocyte counts were different between TKA and UKA patients prior to and following surgery.
Descriptive statistics and bivariate means comparison tests (t-tests and analysis of variance tests) are used to explore how specific characteristics of the mobility experiences of men and women differ (or do not).
