If you increase or see changes in your independent variable, what happens to your dependent variable?
Consider if there's a way to get data to make you reject or disprove your hypothesis. If you can’t reject a hypothesis, it’s not falsifiable. For example, “Apples can create new accounts 25% faster than bananas” isn’t a falsifiable hypothesis because you can’t actually test and reject it. But the hypothesis “Apples contain more water content than bananas” is falsifiable because it can be tested and possibly proven wrong. You can learn more about falsifiability in this video.
Consider what it would mean to find data that rejects or disproves your hypothesis. Would that knowledge still be valuable to you, your stakeholders, or the product?
For more in-depth help, check out this resource or this presentation