For example, if we want to evaluate the effect of three different antihypertensive drugs on three different group of human volunteers, then we will use ANOVA test to evaluate about any significant difference between groups. Repeated measure ANOVA is used when groups to be compared are defined by multiple factors. One-way ANOVA is used when groups to be compared are defined by just one factor.
This test is used to compare the mean of three or more than three groups. This test is used when the SD of two means is almost the same or SD of one group is not twice greater or lesser than that of other. The data should be normally distributed and quantitative. to compare the blood sugar of two independent groups. Unpaired t-test is used to compare the means of two independent groups, e.g. to compare the blood sugar before and after the administration of a drug. Paired t-test is used when one group serves as its own control, e.g. This test is used to compare the two means and is used for small samples ( n <30). Therefore, this test is known as the Student's t-test. He chose his pseudonym as “student” because his company did not allow its scientists to publish confidential data. This is a parametric test which was described by WS Gossett. Tests of significance Parametric tests Student's t- test
The lists of various parametric and nonparametric tests are given in Table 1. Parametric tests use parameters like mean, SD, and standard error of mean for analysis. Only when this transformation is not possible, nonparametric tests should be used. In such scenarios, data transformation technique may be applied to convert skewed data into normal data. Sometimes data does not follow normal distribution and is skewed.
It is always preferable to use parametric test as these tests are more robust. ☑ Standard deviation (SD) covers 68% and ± 2 SD covers 95% of the values in the distribution. Normal distribution is characterized by a smooth bell-shaped symmetrical curve. Parametric test is applied when data is normally distributed and not skewed. Statistical tests can be broadly classified as parametric and nonparametric tests. In the present article, we will discuss about selection and interpretation of statistical tests. We require some basic information for selection of appropriate statistical test such as objectives of the study, type of variables, type of analysis, type of study design, number of groups and data sets, and the type of distribution. Selection of statistical test is not a rocket science and it is based on some assumptions. Postgraduate medical students are often confused in the selection and interpretation of statistical tests during their thesis or research projects.
0 kommentar(er)
