Key Differences Between Descriptive and Inferential Statistics in Data Science

Understanding the differences between descriptive and inferential statistics is important for working with data in data science. Let’s break it down in simple terms.

1. Definitions:

   • Descriptive Statistics: This helps us summarize and organize data. It gives us a clear picture of what the data looks like. For example, it uses numbers like the average (mean), the middle value (median), and the most common value (mode).
   • Inferential Statistics: This takes things a step further. It uses a smaller group of data (a sample) to make guesses or general statements about a larger group (a population). This includes methods like testing ideas (hypothesis testing) and making guesses about future data (regression analysis).

2. Purpose:

   • Descriptive Statistics: Its goal is to present information in a clear and simple way. For example, if you have 100 scores from students, descriptive statistics help you see how the scores spread out and what the average score is.
   • Inferential Statistics: The aim here is to make predictions or test ideas based on the sample data. For example, you could take a sample of 200 people's heights to predict the average height of a whole group of 10,000 people.

3. Applications:

   • Descriptive Statistics: This is used to explore data and find patterns. Some key terms are:
     • Mean (the average)
     • Variance (how spread out the numbers are)
   • Inferential Statistics: This is used to test ideas (like comparing scores) and create models that can predict outcomes.

4. Examples:

   • Descriptive: A bar graph that shows how many students got each score on an exam.
   • Inferential: Estimating a range for what the average score might be for all students based on data from a sample.

In short, descriptive statistics helps us summarize data, while inferential statistics helps researchers make guesses and draw conclusions about larger groups based on smaller samples.