What Is the Null Hypothesis? (And Why You Might Be Thinking About It Backwards)
If you’ve ever found yourself second-guessing the meaning of the null hypothesis, you’re not alone.
In fact, many people (including seasoned professionals) mix up what the null hypothesis actually represents. It’s easy to do. On the surface, it feels like the null should reflect the idea you’re trying to prove. But statistically, it’s often quite the opposite.
Let me explain.
The Common Misunderstanding
One student recently shared this thought process:
“I assumed the null hypothesis represented the true value or outcome that we are trying to prove or disprove with experimentation. For example, if I hypothesize that combining Metal 1 and Metal 2 increases tensile strength, and it does, then the null was correct, right?”
Logical? Yes. Statistically accurate? Not quite.
What the Null Hypothesis Really Means
In statistics, the null hypothesis (H0) is the assumption that nothing has changed. It proposes that any difference you observe in your experiment is due to chance, not due to the factor you’re testing.
Using the metal example:
- Null Hypothesis (H0): Combining Metal 1 and Metal 2 does not increase tensile strength. Any observed change is due to random variation.
- Alternative Hypothesis (H1): Combining Metal 1 and Metal 2 does increase tensile strength.
You run your experiment to gather evidence. If your results show a statistically significant improvement in tensile strength, you have enough evidence to reject the null hypothesis. That doesn’t mean the null was “wrong” in the moral sense. It means the data supports the alternative.
Why This Matters in Lean Six Sigma
Hypothesis testing is foundational to the Analyze phase of DMAIC. Making decisions based on data, not assumptions, is critical. Misunderstanding what you’re testing can lead to incorrect conclusions and wasted resources.
When performing root cause analysis, for example, we often test whether a factor (like a new material or process) actually affects the output. The null always assumes no effect. Your job is to find statistical evidence that says otherwise.
Practical Example
Suppose you work in manufacturing and want to improve the strength of a product. You try combining two metals.
- Null Hypothesis: The combination has no effect on strength.
- Alternative Hypothesis: The combination improves strength.
You collect tensile strength data for samples made with and without the metal combo. If your analysis shows a p-value less than your significance level (often 0.05), you reject the null.
This doesn’t prove the alternative hypothesis 100%. But it does say the result is unlikely to be due to chance.
Key Takeaways
- The null hypothesis always assumes no effect or no difference.
- You don’t prove the null right or wrong. You gather data to reject or fail to reject it.
- Failing to reject doesn’t mean the null is true. It just means the evidence isn’t strong enough.
Want to go deeper into tools like hypothesis testing, p-values, or statistical significance? Check out these resources on hypothesis testing and ANOVA.
Final Thought
Good science starts with good questions. But great process improvement starts with testing those questions the right way. And that starts with getting your hypothesis straight.
When you’re ready, there are a few ways I can help:
First, join 30,000+ other Six Sigma professionals by subscribing to my email newsletter. A short read every Monday to start your work week off correctly. Always free.
—
If you’re looking to pass your Six Sigma Green Belt or Black Belt exams, I’d recommend starting with my affordable study guide:
1)→ 🟢Pass Your Six Sigma Green Belt
2)→ ⚫Pass Your Six Sigma Black Belt
You’ve spent so much effort learning Lean Six Sigma. Why leave passing your certification exam up to chance? This comprehensive study guide offers 1,000+ exam-like questions for Green Belts (2,000+ for Black Belts) with full answer walkthroughs, access to instructors, detailed study material, and more.