Here at Process Street, we’re always advocating for companies to use data to help in making important decisions.
But data on its own is not massively useful.
I could run an experiment right now and gather loads of data. But if that experiment was run poorly then my data will be poor. Which means any readings of that data will be poor too, leading to poor decisions.
Alternatively, I could run a really well structured piece of research and gather some great data, but if I don’t know how to properly analyze that data then my conclusions won’t be very good.
Simply having large data sets is not enough.
We need to structure our research well and then be able to interpret the results with a degree of rigour. Fortunately, having a good working knowledge of P-Values can help us iron out some alarmingly common mistakes. It can teach us:
- How to set up an experiment for meaningful data
- The importance of measuring your existing hypothesis against an alternative
- When results really are statistically significant, instead of just looking good
This knowledge will help us make better decisions and lead to greater success.
In this Process Street article, we’ll look at 4 key areas:
- What are P-Values?
- How do you calculate P-Values?
- Examples of P-Values in practice – A/B testing
- Why you need to set up a research process