Descriptive Statistics in SPSS in Hindi

Many students and researchers struggle with SPSS when trying to calculate basic descriptive statistics. If you’re one of them—and you prefer learning in Hindi—this post is for you.

In this tutorial, I walk you through the full process of performing descriptive statistics in SPSS, explained step-by-step in clear and simple Hindi. You don’t need prior experience, and the video takes only a few minutes to follow.

📘 Understanding Descriptive Statistics

Before we start, let’s define what we’re doing. Descriptive statistics help you:

Summarize your dataset
Get a quick overview of data patterns
Prepare for further analysis like t-tests or ANOVA

The most common measures include:

Mean (average)
Standard Deviation (how spread out the data is)
Minimum and Maximum Values

These are the foundation for most academic and real-world data analysis.

🎥 Watch the Full Video Tutorial (Hindi)

Below is the video guide, which shows the process inside SPSS in just 3 simple steps:

✅ Key Takeaways from the Video

Open and Review Your Dataset
You’ll learn how to import and inspect your dataset in SPSS. This step ensures your variables are ready for analysis.
Run Descriptive Statistics
I demonstrate how to use the Analyze > Descriptive Statistics > Descriptives menu to generate key metrics instantly.
Understand the Output Table
Each result in the SPSS output is broken down for you. I explain what the values mean and how to interpret them in your context.

📌 Why It Matters

Many learners focus too much on complex statistical models before mastering the basics. Descriptive stats allow you to:

Understand your data at a glance
Spot errors or outliers early
Build a strong foundation for further tests

If you’re working on a project, dissertation, or paper, this skill is a must.

🗣️ Why in Hindi?

There are thousands of tutorials online—but very few in Hindi that explain statistical methods step-by-step. This video was created for learners who are more comfortable in Hindi and want a clear, practical approach.