These statistical methods involve a few steps:
Posted: Tue Dec 24, 2024 8:32 am
In essence, a hypothesis is a theory about how or why a specific effect occurs. In an ecommerce context, a simple hypothesis could be that displaying cart abandonment popups will result in a better conversion rate.
Once you have a theory like that, you can use a statistical hypothesis test to see whether what you predict (an increased conversion rate, in the above example) will actually occur.
A commonly used technique in hypothesis testing is A/B testing. This is where two versions of an existing site or app are compared to determine which one performs better in terms of user engagement or conversion rate.
Hypothesis Experimentation illustration
Statistical analysis is fundamental vietnam email list to hypothesis testing because it’s the only way to tell whether the effect you’ve observed is the result of something significant or simply down to random chance.
Formulating a hypothesis that claims changing one variable will affect another variable.
Formulating a null hypothesis, which states the opposite (that no significant difference will occur).
Conducting hypothesis tests using sample data to determine the statistical power of each of your two hypotheses.
Deciding whether to accept or reject the null hypothesis based on its statistical significance.
If we use A/B testing as an example, the null hypothesis will be that the variation you’re testing will have no effect, while the alternative hypothesis will be that the variation will cause an effect.
Once you have a theory like that, you can use a statistical hypothesis test to see whether what you predict (an increased conversion rate, in the above example) will actually occur.
A commonly used technique in hypothesis testing is A/B testing. This is where two versions of an existing site or app are compared to determine which one performs better in terms of user engagement or conversion rate.
Hypothesis Experimentation illustration
Statistical analysis is fundamental vietnam email list to hypothesis testing because it’s the only way to tell whether the effect you’ve observed is the result of something significant or simply down to random chance.
Formulating a hypothesis that claims changing one variable will affect another variable.
Formulating a null hypothesis, which states the opposite (that no significant difference will occur).
Conducting hypothesis tests using sample data to determine the statistical power of each of your two hypotheses.
Deciding whether to accept or reject the null hypothesis based on its statistical significance.
If we use A/B testing as an example, the null hypothesis will be that the variation you’re testing will have no effect, while the alternative hypothesis will be that the variation will cause an effect.