Poor experimental design can lead to misleading results, wasted resources, and even product failures. Industries like pharmaceuticals, food manufacturing, and aerospace rely on statistical methods to ensure accuracy and reliability in their research.
Common Statistical Pitfalls
- Ignoring Interactions Between Variables – In drug development, testing one ingredient at a time can lead to incorrect formulations. Pfizer uses Design of Experiments (DOE) to identify how multiple drug components interact.
- Small Sample Sizes – In clinical trials, too few participants can produce statistically insignificant results, leading to failed FDA approvals.
- Confirmation Bias – In market research, companies might unintentionally interpret data in a way that supports pre-existing beliefs, leading to poor product decisions.
How to Avoid These Pitfalls - Use factorial designs to study multiple variables simultaneously.
- Ensure adequate sample sizes for statistical power.
- Rely on blind testing and randomized trials to prevent bias.
Conclusion
From biotech to automotive, avoiding statistical pitfalls is essential for producing valid, actionable insights and ensuring efficient decision-making.