A Statistical Approach to Real-time Reproductive Rate Estimates of the COVID-19 Pandemic Based on the Statistical Powers in terms of Different Sample Sizes, Effect Sizes and Standard Deviations for Independent/Paired Samples t-test
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Reproductive rate estimate of the COVID-19 pandemic, Monte Carlo simulation, independent/paired samples t-test, statistical power, sample size, effect sizeAbstract
In this study, appropriate statistical powers for independent/paired samples t-test applications are tried to be determined using Monte Carlo simulation method in terms of different sample sizes, effect sizes and standard deviations. In the empirical part of this study, based on the Monte Carlo simulation method, firstly “independent samples t-test” is performed for testing the averages of real-time reproductive rate estimates of the COVID-19 Pandemic data between African and European Continents including 45 countries. And then “paired samples t-test” is performed for testing the averages of real-time reproductive rate estimates of the COVID-19 Pandemic data between December 2021 and January 2022 as the paired observations of 31 different countries taken from the European Continent.
As the principal results and major conclusions of this study, belonging to the independent samples t-test results, it is determined that the statistical power decreased as the standard deviation increased. The statistical power increased as the effect size widened at a fixed sample size and standard deviation value. Belonging to the paired samples t-test results, it is determined that the statistical power decreased as the significance level decreased. In addition, statistical powers for the paired samples t-test with fixed sample size and effect size are estimated to be higher than for the independent samples t-test. Also, statistical powers for the paired samples t-test with fixed sample size and effect size are found higher than for the independent samples t-test.
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