A test hypothesis is a focused statement of conjecture. In actuality, the statement may be true or untrue. Through testing and analysis, the objective of hypothesis testing is to prove or disprove the statement and be in a position to indicate the reliability of conclusion.
A good test hypothesis will be written as a statement or question that specifies:
A website owner thinks that by making changes to the images of a product range (hats in this example) they can increase revenue. Before they go ahead and conduct that change, they decide to conduct AB testing and test the hypotheses that:
In the example above, the dependent variable are the visitors to the hats category webpage, the independent variables are the images shown to the visitors, and finally, the effect prediction is the increase in online hat revenue.
Following a test, the expectation is that either:
H0 is accepted & H1 is rejected
H1 is rejected & H0 is rejected.
Below is the main terminology to bear in mind when executing a test hypothesis:
Controlling the risks of these errors, we would usually test to a Level of statistical significance: This is a calculation that determines the level of confidence that can be made about test obersvation. 95% statistical significance level is the recommendation and means there is only a 5% probability that the result is due to chance and the risk of type 1 or 2 errors mitigated.
Not every test hypothesis is always possible to prove as true, as many times, a test hypothesis can reject the alternative hypothesis and it’s OK. In the end, conducting a test hypothesis is a very important part of your conversion rate optimisation strategy as it removes the guessing element of your decision-making and allows you to make statistical decisions based on scientific evidence..
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