In medicine, we don’t just collect data—we interpret it to make informed decisions. Whether analyzing patient records, conducting clinical trials, or evaluating disease trends, we rely on two main branches of statistics: descriptive and inferential. Understanding these concepts is crucial for anyone involved in research or clinical decision-making.

Descriptive vs. Inferential Statistics: What’s the Difference?

Statistics helps us summarize, analyze, and draw conclusions from data. But not all statistical methods serve the same purpose.

1. Descriptive Statistics – “Telling the Story of the Data”

   • Focuses on summarizing and organizing raw data.

   • Provides an overview without drawing deeper conclusions.

   • Examples: A hospital reports the average blood pressure of ICU patients or the percentage of smokers in a study.

     
     

2. Inferential Statistics – “Making Predictions and Testing Hypotheses”

   • Uses a sample of data to make generalizations about a larger population.

   • Helps us test hypotheses and determine the probability of an event occurring.

   • Examples: Using a sample of 500 patients to estimate the effectiveness of a new drug for all patients with the disease.

Key Difference?

Descriptive statistics organizes and presents the data, while inferential statistics helps us interpret it and make decisions.

Types of Data: The Foundation of Statistical Analysis

Before applying statistical methods, it’s essential to understand the different types of data—because the type of data determines which statistical tests can be used.

1. Categorical (Qualitative) Data: data that represents groups or categories rather than numerical values.

   • Subtypes:

     • Nominal Data: Categories without a natural order (e.g., eye color, gender).

     • Ordinal Data: Categories with a meaningful order (e.g., cancer stages: Stage I, II, III, IV).

   • Examples in Medicine:

     • Blood type (A, B, AB, O), disease status (positive/negative), and medication type (antibiotic, antiviral, etc.)

     
     

2. Numerical (Quantitative) Data: data that consists of measurable numbers.

   • Subtypes:

     • Discrete Data: Whole numbers only (e.g., number of hospital visits).

     • Continuous Data: Any value within a range (e.g., cholesterol level).

   • Examples in Medicine:

     • Patient age (18 years | discrete), height ( 170.5 cm | continuous ), and white blood cell count.

       
       

Why Does This Matter?

The type of data you collect determines how you analyze it. Some statistical methods only work with numerical data, while others are designed for categorical data. For example, you wouldn’t calculate an average for blood types (since A, B, AB, and O are categories, not numbers).

Key Takeaways

• Descriptive statistics summarizes data, while inferential statistics helps us make predictions and test hypotheses

• Categorical data groups observations into categories, while numerical data consists of measurable numbers.

• Knowing your data type is crucial for choosing the right statistical methods.

Up Next:

Now that we’ve classified data, our next post will cover measures of central tendency (mean, median, and mode) and variability (range, variance, standard deviation)—essential tools for analyzing medical data.