1. Thoughts on the Future of Statistics Teaching in the light of Big Data
Louisiana State University - Stephenson Dept. of Entrepreneurship and Decision Sciences
Helmut Schneider, PhD, Xuan Wang

2. Overview Hypothesis Testing Causal Inference Big Data
Causal Inference, Miguel A. Hernan, James M. Robins
Judea Pearl Causal
Judea Pearl:Causal Inference:
Miguel A. Hernan, James M. Robins, Causal Inference

3. Hypothesis Testing Formulate a Theory State Hypothesis: Ho versus H1
Take a sample
Compute statistics
Make decision
What is the reason for these steps?

4. Problem Identification Traditional Data Sources
Big Data
Traditional Data Sources
Small volume – low statistical power
Limited variety – Biased estimates
Low velocity – estimates may not be valid in the future
Untapped Sources
High volume – high statistical significance - small p value
High variety – small bias
High velocity – dynamic update of estimates

5. Statistical Significance versus Practical Significance
Accounting faculty research…
Auditors take samples…

6. Statistical Significance versus Practical Significance
Cancer Doctors Cite Risks of Drinking Alcohol
12 million women and over a quarter of a million breast cancer cases
Statistical significance versus
practical significance
Risk Ratio 9% versus
Risk Difference 0.18 percentage points.

7. Big Data Implications
Big data makes everything statistically significant.
This is how the real world works.
Implications for teaching statistics
Need for students to understand practical significance versus statistical significance.

8. Causal Inference Correlation is not causation.
Statisticians only deal with correlations. But yet they also teach students that there is spurious correlation.
Myth: In Big Data correlation is causation.
Need for students to learn to judge causation.

9. Even in Big Data Correlation is not causation!
Need for students to learn about causality. 9

10. When can we Make Causal Claims
Randomized Designs
Observational Data
Well – Defined Treatment
Positivity
Exchangeability 10

11. Confounding: Directed Acyclic Graphs (DAG)
Treatment
Outcome
Need for students to learn about confounding and DAGs.
Confounder
Factor

12. Statistical Significance versus Unbiased Estimates
Causality
Unbiased Estimates
Timely Estimates
Variety
Velocity
Statistical Significance
Volume

13. Causal Inference

14. Conclusions
Students need to learn about the reasons for using hypothesis testing in todays Big Data environment.
Need for students to learn to judge practical significance versus statistical significance.
Need for students to learn about DAGs.
Students need to learn about methods to establish causation.