Q Value

Understanding p value can be confusing. People who get it have an edge over those who don’t.

To make it easier, use q-value.

q = 1 – p. It tells us the probablity (95% or above) that the experiment worked.

Suppose you run an experiment and get p = 0.04. That means q = 0.96. So, there is a 96% chance that your experiment is successful.

Every experiment starts with a hypothesis. For example: “Adding Feature X will improve LTV.”

To test this, we create a null hypothesis, the opposite. So: “Adding Feature X does not improve LTV.”

The p value tells us how likely the null hypothesis is true.

If p is low (less than 5%), we say the null is false. That means our idea is likely right. If p is high, we keep the null. Our idea might not work.

Example:

You want to test if changing the button color will increase clicks.

Null hypothesis: “Changing the color of the button will not increase clicks.”

You run the experiment and get the p-value.

If p is high (say, above 0.05), it means your change didn’t help. If q is high (say, 95% or more), your change worked.

Think of it like this:

High p → Experiment likely failed

High q (or Low p) → Experiment likely succeeded

Use q to simplify decisions. It it more intuitive and helps your team move faster.