Welcome to the discussion on one of the most important—but often overlooked—aspects of research and project work: data analysis.
Whether you're working on clinical research, a thesis project, or business analytics, data analysis plays a crucial role in generating valid, reliable conclusions. However, many researchers face similar obstacles during the process.

Let’s explore some common issues in data analysis and invite discussion on how to avoid or resolve them.

🔍 Common Issues:

1. Missing or Incomplete Data
   Often datasets contain gaps or blank entries, leading to biased or inaccurate results if not handled properly. How do you deal with missing data — imputation, exclusion, or modeling?

2. Incorrect Statistical Test Selection
   Applying the wrong statistical test can completely mislead your findings. Many users struggle to choose between t-tests, chi-square, ANOVA, regression, etc. When in doubt, do you consult a statistician?

3. Violation of Assumptions
   Statistical tests often require assumptions (normality, independence, equal variances). Ignoring these can invalidate results. Do you routinely check these assumptions before proceeding?

4. Overfitting/Underfitting Models
   In machine learning or regression models, fitting too closely to training data (overfitting) or too loosely (underfitting) leads to poor generalization. What tools do you use to check model performance?

5. Misinterpretation of Results
   Confusing correlation with causation, overreliance on p-values, or ignoring effect size and confidence intervals are very common mistakes. How do you ensure your findings are interpreted correctly?