Key Principles to Know:

1. Types of Data

   • Quantitative (e.g., height, age, blood pressure)

   • Qualitative (e.g., gender, disease status)
     Understanding data types helps in selecting the right statistical tests.

2. Measures of Central Tendency

   • Mean, Median, Mode
     These describe where the center of your data lies and are critical for summarizing data.

3. Measures of Dispersion

   • Range, Variance, Standard Deviation
     These show how spread out your data is — essential for understanding variability.

4. Probability & Distributions

   • Normal distribution, binomial distribution
     Knowing the shape and characteristics of your data helps in applying correct models and tests.

5. Hypothesis Testing

   • Null vs. Alternative Hypothesis, p-values, confidence intervals
     These form the backbone of inferential statistics and determine the significance of your results.

6. Types of Errors

   • Type I (false positive) and Type II (false negative)
     Understanding these helps reduce bias and improves decision-making in research.

7. Correlation vs. Causation
   Just because two variables move together doesn’t mean one causes the other!
   This principle is often misunderstood but critically important in interpreting results.