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WHEN TO USE WHAT STATISTICAL TEST IN RESEARCH

There are several statistical test types for analyzing Research Data. When to use what is often the challenge. This piece provides a simplification 

1️⃣t-test:

- Use when: You want to compare the means of two groups to determine if there's a significant difference.
- Example: You want to compare the average score of students who received traditional teaching vs. those who received innovative teaching.

2️⃣ANOVA (Analysis of Variance):

- Use when: You want to compare the means of three or more groups to determine if there are significant differences.
- Example: You want to compare the average score of students from different schools to determine if there are significant differences in their performance.

3️⃣Regression (Simple and Multiple):

- Use when: You want to examine the relationship between a dependent variable and one or more independent variables.
- Example: You want to examine the relationship between hours studied and exam scores (simple regression), or the relationship between hours studied, exam scores, and student motivation (multiple regression).

4️⃣Chi-squared test:

- Use when: You want to determine if there's a significant association between two categorical variables.
- Example: You want to determine if there's a significant association between smoking and lung cancer.

5️⃣Wilcoxon rank-sum test (Mann-Whitney U test):

- Use when: You want to compare the distributions of two independent groups.
- Example: You want to compare the distribution of scores between students who received traditional teaching and those who received innovative teaching.

6️⃣Kruskal-Wallis H test:

- Use when: You want to compare the distributions of three or more independent groups.
- Example: You want to compare the distribution of scores among students from different schools.

7️⃣Friedman test:

- Use when: You want to compare the distributions of three or more related groups.
- Example: You want to compare the distribution of scores among students at different time points.

8️⃣Pearson correlation coefficient:

- Use when: You want to examine the linear relationship between two continuous variables.
- Example: You want to examine the relationship between hours studied and exam scores.

9️⃣Spearman rank correlation coefficient:

- Use when: You want to examine the relationship between two variables when data is not normally distributed.
- Example: You want to examine the relationship between ranking of favorite foods and ranking of nutritional value.

🔟Kendall's tau correlation coefficient:

- Use when: You want to examine the relationship between two variables when data is ordinal or categorical.
- Example: You want to examine the relationship between socioeconomic status and education level.

1️⃣1️⃣ARIMA models:

- Use when: You want to forecast future values in a time series data.
- Example: You want to predict stock prices based on past trends.

1️⃣2️⃣Exponential smoothing (ES):

- Use when: You want to forecast future values in a time series data with a simple exponential smoothing method.
- Example: You want to predict sales based on past trends.

1️⃣3️⃣Seasonal decomposition:

- Use when: You want to decompose time series data into trend, seasonality, and residuals.
- Example: You want to analyze website traffic data to identify seasonal patterns.

1️⃣4️⃣Kaplan-Meier estimator:

- Use when: You want to estimate the survival function of a population.
- Example: You want to analyze the survival rate of patients with a specific disease.

1️⃣5️⃣Cox proportional hazards model:

- Use when: You want to examine the relationship between covariates and survival time.
- Example: You want to investigate the effect of treatment on survival time.

1️⃣6️⃣Log-rank test:

- Use when: You want to compare the survival curves of two or more groups.
- Example: You want to compare the survival rates of patients with different treatments.

1️⃣7️⃣K-means clustering:

- Use when: You want to group similar observations into clusters based on features.
- Example: You want to segment customers based on buying behavior.

1️⃣8️⃣Hierarchical clustering:

- Use when: You want to group similar observations into clusters based on features, with a hierarchical structure.
- Example: You want to analyze gene expression data to identify clusters of genes.

1️⃣9️⃣DBSCAN (density-based spatial clustering of applications with noise):

- Use when: You want to group similar observations into clusters based on features, with noise handling.
- Example: You want to analyze spatial data to identify clusters of high density.

2️⃣0️⃣Principal component analysis (PCA):

- Use when: You want to reduce the dimensionality of a dataset by identifying principal components.
- Example: You want to analyze stock prices to identify principal components of variation.

2️⃣1️⃣Discriminant analysis:

- Use when: You want to predict group membership based on multivariate data.
- Example: You want to predict customer churn based on usage patterns.

2️⃣2️⃣Canonical correlation analysis:

- Use when: You want to examine the relationship between two sets of multivariate data.
- Example: You want to investigate the relationship between personality traits and behavior.

2️⃣3️⃣Bayesian inference:

- Use when: You want to update probabilities based on new data.
- Example: You want to update the probability of a hypothesis based on new evidence.

2️⃣4️⃣Bayesian regression:

- Use when: You want to model the relationship between variables using Bayesian methods.
- Example:

2️⃣5️⃣Bayesian networks:

- Use when: You want to model complex relationships between variables using Bayesian methods.
- Example: You want to model the relationship between genes and diseases.

2️⃣6️⃣Decision trees:

- Use when: You want to classify observations based on a tree-like model.
- Example: You want to predict customer churn based on usage patterns.

2️⃣7️⃣Random forests:

- Use when: You want to classify observations based on an ensemble of decision trees.
- Example: You want to predict disease diagnosis based on symptoms.

2️⃣8️⃣Support vector machines (SVMs):

- Use when: You want to classify observations based on a hyperplane.
- Example: You want to predict customer churn based on usage patterns.

2️⃣9️⃣Cluster analysis:

- Use when: You want to group similar observations into clusters based on features.
- Example: You want to segment customers based on buying behavior.

3️⃣0️⃣Factor analysis:

- Use when: You want to reduce the dimensionality of a dataset by identifying underlying factors.
- Example: You want to analyze survey data to identify underlying factors of satisfaction.

3️⃣1️⃣Survival analysis:

- Use when: You want to analyze the time-to-event data.
- Example: You want to analyze the survival rate of patients with a specific disease.

3️⃣2️⃣Time-series analysis:

- Use when: You want to analyze data that is ordered in time.
- Example: You want to analyze stock prices to identify patterns and trends.

3️⃣3️⃣Non-parametric tests:

- Use when: You want to analyze data without assuming a specific distribution.
- Example: You want to compare the median scores of students who received traditional teaching vs. those who received innovative teaching.

3️⃣4️⃣Machine learning algorithms:

- Use when: You want to predict outcomes or classify observations based on large datasets.
- Example: You want to predict customer churn based on usage patterns.

The specific test or technique used depends on the research question, data type, and study design.




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