How is Statistics Used in Data Science?
Last Updated :
23 Jul, 2025
Statistics plays a important role in data science as it helps data scientists understand complex data, identify relationships between variables and build models that solve real-world problems. Rather than relying on assumptions, statistics brings confidence to data-driven decision-making.
It is used to:
- Summarize large datasets (averages, variance, distributions)
- Understand relationships between features and outcomes
- Detect patterns, anomalies and data quality issues
- Support machine learning model building and evaluation
Let’s walk through a practical example.
An education company wants to identify students who are likely to perform poorly in the final exam. Here’s a small sample of the dataset:
| StudentID | StudyHours | Attendance (%) | PreviousScore | FinalResult |
|---|
| S001 | 2 | 60 | 55 | Fail |
| S002 | 5 | 85 | 70 | Pass |
| S003 | 1 | 40 | 45 | Fail |
| S004 | 4 | 90 | 75 | Pass |
| S005 | 3 | 65 | 60 | Fail |
Now, Let's Apply Statistics
1. Failure Rate
- Total students = 5
- Failed = 3
- Failure Rate = (3 / 5) × 100 = 60%
2. Average Study Hours
- Study hours of all students = 2, 5, 1, 4, 3
- Average = (2 + 5 + 1 + 4 + 3) / 5 = 3 hours
3. Average Attendance by Result
- Failed students: (60 + 40 + 65)/3 = 55%
- Passed students: (85 + 90)/2 = 87.5%
4. Correlation Observation
We observe that lower study hours and lower attendance seem to correspond with failed results. This hints at a positive correlation between both features and final outcome.
What Can We Infer from These Stats?
- Students who studied less and had poor attendance were more likely to fail.
- On average, failed students had only 55% attendance, compared to 87.5% for those who passed.
- The average study time across all students was only 3 hours indicating a possible overall lack of preparation.
A data scientist can use these findings to engineer better features like attendance thresholds or feed them into predictive models that flag students who needs early intervention.
While applying these statistical methods in data science, we commonly use Python libraries like NumPy, Pandas, math and scipy. These tools make it easier to process, clean and analyze data effectively.
| Tool/Concept | Use in Data Science |
|---|
| Mean, Median, Mode | Summarize feature distributions |
| Standard Deviation | Measure feature spread and variability |
| Correlation | Identify dependencies between variables |
| Regression | Build predictive models |
| Hypothesis Testing | Test assumptions about the data |
| Z-Scores, Percentiles | Detect outliers or rank data points |
| Probability Distributions | Understand data behavior (normal, binomial) |
In data science, statistics is the foundation that turns raw data into reliable insights that guides everything from exploration to model building and helping us solve real-world problems with confidence.
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