📊Statistical Test

Statistical testing is a cornerstone of data analysis, providing a systematic approach to evaluate hypotheses and draw reliable conclusions from empirical observations. In this analytical toolkit, various tests serve distinct purposes, catering to the diverse needs of researchers and analysts. The t-test, ANOVA test, Spearman’s Rank Correlation, Pearson’s Correlation, Chi-Squared Test, Shapiro-Wilk Test, Kruskal-Wallis H Test, and Friedman Test represent a comprehensive suite of statistical tools.

The t-test is fundamental for comparing means between two groups, while the ANOVA test extends this capability to multiple groups. Spearman’s Rank Correlation and Pearson’s Correlation assess the relationships between variables, offering insights into the strength and nature of associations. The Chi-Squared Test examines the independence of categorical variables, a crucial consideration in contingency table analysis. The Shapiro-Wilk Test evaluates data normality, influencing subsequent parametric analyses.

For scenarios where assumptions of normality are not met, the Kruskal-Wallis H Test serves as a non-parametric alternative to ANOVA, and the Friedman Test extends this comparison to repeated measurements. Together, these tests empower analysts to explore, validate, and interpret data across a spectrum of research questions and experimental designs, contributing to the robustness and reliability of statistical analyses.

Statistical tests we offer

T-Test

ANOVA Test

Spearman Rank

Pearson's Correlation

Chi-Squared Test

Shapiro-Wilk Test

Kruskal-Wallis H Test

Friedman Test

1. T-Test:

   • The T-Test is employed when comparing the means of two groups to determine if the observed differences are statistically significant. It is commonly used in hypothesis testing when dealing with small sample sizes.

2. ANOVA Test (Analysis of Variance):

   • ANOVA is utilized when comparing means across three or more groups. It assesses whether the observed differences in group means are likely due to actual differences in population means or if they could have occurred by chance.

3. Spearman’s Rank Correlation:

   • Spearman’s Rank Correlation is a non-parametric test that assesses the strength and direction of monotonic relationships between two variables. It is particularly useful when dealing with ordinal or non-normally distributed data.

4. Pearson’s Correlation:

   • Pearson’s Correlation measures the strength and direction of a linear relationship between two continuous variables. It assumes that the data is normally distributed and is sensitive to outliers.

5. Chi-Squared Test:

   • The Chi-Squared Test is used to determine if there is a significant association between two categorical variables. It compares the observed distribution of data with the distribution that would be expected if there were no association.

6. Shapiro-Wilk Test:

   • The Shapiro-Wilk Test is a test for normality, indicating whether a sample follows a normal distribution. It is widely used to check the assumption of normality in statistical analyses.

7. Kruskal-Wallis H Test:

   • The Kruskal-Wallis H Test is a non-parametric alternative to ANOVA, used when comparing three or more independent groups. It assesses whether there are significant differences in the medians of the groups.

8. Friedman Test:

   • The Friedman Test is a non-parametric alternative to repeated measures ANOVA. It assesses whether there are significant differences in the medians of three or more related groups over different treatments or time points.