2019
DOI: 10.4103/aca.aca_94_19
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Application of student's t-test, analysis of variance, and covariance

Abstract: Student's t test (t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. The Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. In ANOVA, first gets a common P value. A significant P value of the ANOVA test indicates for at least one pair, between which the mean difference was statistically significant. To… Show more

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Cited by 485 publications
(306 citation statements)
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References 8 publications
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“…We used the form average value ± SD to present a continuous variable. We used one-way ANOVA [ 19 ] and Student's t -test [ 20 ] for multiple comparisons. All the analyses were performed in the software GraphPad Prism, v5.0 (GraphPad, La Jolla, CA, USA).…”
Section: Methodsmentioning
confidence: 99%
“…We used the form average value ± SD to present a continuous variable. We used one-way ANOVA [ 19 ] and Student's t -test [ 20 ] for multiple comparisons. All the analyses were performed in the software GraphPad Prism, v5.0 (GraphPad, La Jolla, CA, USA).…”
Section: Methodsmentioning
confidence: 99%
“…The obtained data corresponded to data normally distributed and were statistically analyzed by one-way analysis of variance (ANOVA) followed by Tukey's test of multiple comparisons using Instat ® software and the level of significance was set to p < 0.05. The unilateral ANOVA test can also be used to evaluate multiple measures of the same variable taken on the same subjects or in combined individuals, either under different conditions or over more than two periods of time (Mishra et al, 2019).…”
Section: Resultsmentioning
confidence: 99%
“…For categorical variables, we conducted chisquare test or Fisher's exact test to indicate whether presence of conditions differed across disease groups. Based on the analyses, we selected variables with the p-values less than 0.05 to predict COVID-19 severity by a nominal logistic regression with adjustments to age and gender [8][9][10][11][12] . All statistical analyses were carried using R version 3.6.2.…”
Section: Resultsmentioning
confidence: 99%