As far as research is concerned, good research is not just about collecting good information and presenting it to the audience, which is gained after a proper and reliable statistical Tests
Basically, it involves describing the data and how it is interpreted, tabulated, and represented graphically.
Here are a few of the best and most valuable statistical tests in research that you should be aware of, including when to use them, descriptions of each test, and an example of how to conduct each test using software and tools:
Most Valuable Statistical Tests in Research
| Statistical Test | When to Use | Description | Example |
|---|---|---|---|
| Descriptive Statistics | To summarize dataset features | Provides measures of central tendency and variability | Calculating average age and standard deviation of students in a class |
| Z-Test | Large samples, known population variance | Compares sample mean to known population mean | Testing if average customer spending differs from the known population average |
| T-Test (Student’s) | Smaller samples, unknown population variance | Compares means from two groups | To compare girls’ heights with boys’ heights |
| Paired T-Test | Comparing two related samples | Assesses difference in means between paired observations | To compare weight of infants before and after a feed |
| Welch’s T-Test | Unequal variances and/or sample sizes | T-test adaptation for unequal variances | Comparing test scores between two classes with different sizes |
| Chi-Squared Test | Categorical data analysis | Tests association between categorical variables | To assess whether acceptance into medical school is related to applicant’s country of birth |
| Fisher’s Exact Test | Small samples, 2×2 contingency tables | Tests association between classifications | Analyzing if a new drug is effective in a small clinical trial |
| Mann-Whitney U Test | Non-parametric test for two groups | Compares two independent groups | Comparing customer satisfaction scores between two product lines |
| ANOVA | Comparing means of 3+ groups | Analyzes variance between multiple groups | To determine if plasma glucose level differs at one, two, or three hours after a meal |
| Kruskal-Wallis Test | Non-parametric alternative to ANOVA | Compares 3+ independent samples | Comparing satisfaction levels across different departments in a company |
| Pearson’s Correlation | Measuring association between variables | Assesses linear relationship strength | To assess whether plasma HbA1c concentration is related to plasma triglyceride concentration in diabetic patients |
| Spearman’s Rank Correlation | Non-parametric correlation | Measures monotonic relationship strength | Analyzing the relationship between education level and income |
| Simple Linear Regression | Relationship between two variables | Models linear relationship between variables | To see how peak expiratory flow rate varies with height |
| Multiple Regression | One dependent, multiple independent variables | Predicts dependent variable from multiple predictors | To determine how age, body fat, and serum intake influence blood pressure |
| Logistic Regression | Binary dependent variable | Predicts probability of binary outcome | Predicting the likelihood of heart disease based on various health factors |
| Factor Analysis | Identifying underlying factors | Reduces variables to smaller set of factors | Identifying underlying personality traits from a set of survey questions |