Compare the Testing Group Differences using T-tests, ANOVA and Non-Parametric Tests
Compare the Testing Group Differences using T-tests, ANOVA, and Nonparametric TESTS
The main purpose of this blog is to understand the Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures.
Choosing the right test for your data analysis is a very difficult task particularly identifying the Different methods from testing group differences is the Biggest challenging task. It is important to have to in-depth Knowledge to understand and calculate T-tests, ANOVA, and Nonparametric, brief interpretation of the output. In order to choose the right statistical test, when analyzing the data from an experiment, we must have a good understanding of some basic statistical terms and concepts:
Test for Normality
Every data must follow certain distribution. But we have to find the appropriate distribution from goodness of fit test. So, our data is checked through each and every distribution. Hence, goodness of fit test is very tedious. This way of estimation of data is called by parametric tests. Parametric tests always give the reliable estimated value.
If the data follow the normal distribution, then we can use parametric statistical tests. According to the central limit theorem, if the sample size is large, all data must follow the normal distribution.
List of parametric tests and their usage?
- Independent sample t test – Compare means between two groups.
- Paired sample t test – Compare means between related groups.
- ANOVA – Compare the means between two or more distinct groups.
- Pearson correlation coefficient – Relationship between two variables.
List of non-parametric tests and their usage?
- Mann-Whitney U test – Compare mean rank between two groups.
- Kruskal-Wallis test – Compare the mean rank between two or more distinct groups.
- Spearman’s rank correlation – Relationship between two variables.
Comparing Group Means: The T-test and One-way ANOVA Using STATA, SAS, and SPSS
While the t-test is inadequate to comparing means of two groups, one-way ANOVA can compare more than two groups. Therefore, the t-test is considered a special case of one-way ANOVA. These analyses do not, however, necessarily imply any causality (i.e., a causal relationship between the left-hand and right-hand side variables).
Table 1:
Compares the t-test and one-way ANOVA:
T-Test | One-Way ANOVA | |
LHS (Dependent) | Interval or ratio variable | Interval or ratio variable |
RHS (Independent) | Binary variable with only two groups | Categorical variable (MORE THAN 2 GROUPS) |
Null Hypothesis | µ1 = µ 2 | µ1 = µ 2 = µ 3 |
Prob. Distribution | T distribution | F distribution |
Writing a research paper for statistical related paper play a critical role when we are testing group differences using T-tests, ANOVA, and Nonparametric TEST. Choosing the right statistical guidance for your Comparing Group Means will help to complete your research paper as soon as possible.