Statistical Significance in Product Development: A Data Science Approach
Author: Najib Yusuf Ubandiya
Date: October 2, 2025
Associate Data Science Certification: https://www.datacamp.com/certificate/DSA0019891134203
When comparing two groups, it is not enough to rely on what the numbers seem to show. We need a way to judge whether a difference is real or just random variation. This is what a hypothesis test is for.
We start by stating two ideas:
- Null hypothesis (H₀): there is no difference between the groups
- Alternative hypothesis (H₁): there is a real difference
Let's say we are testing a post-surgery recovery app. Early results show users report 25% less pain than non-users, but we need to check if this result is meaningful before recommending it to all 10,000 patients.
A hypothesis test asks: If the app had no effect, how likely is it that we would still see a difference this large just by chance? If this likelihood is very low, we have evidence that the effect is real.
This likelihood is summarised by the p-value. A p-value is a number between 0 and 1 that answers the question:
“If there were no real effect, how surprising would these results be?”
A low p-value means the result would be rare if the null hypothesis were true. For example, a p-value of 0.03 means there is only a 3% chance of getting results this strong by random chance. Many companies use a threshold of 0.05 to decide whether to treat a result as meaningful.
Hypothesis testing helps decision makers move from weak claims like “this looks good” to evidence such as “this result is unlikely to be random”. If p = 0.02, we can support rolling out the app. If p = 0.40, the result is too uncertain, and we should delay investment or collect more data.