Experimentation

Most A/B testing programs produce confident conclusions from insufficient data. The discipline is almost entirely in what you do before launch.

Before running

Hypothesis with a mechanism. "Moving the pricing table above the fold will raise trial starts, because visitors currently leave before seeing pricing." Not "let's try a green button."
One primary metric, chosen in advance. Secondary metrics are context, never the verdict.
Sample size calculated in advance, from your baseline rate and the smallest lift that would change a decision. If the required sample is unreachable, do not run the test — decide by judgment and say so.
Duration set in advance, covering at least one full weekly cycle, and two if the buying cycle is long.
Guardrail metrics that would make you reject a win: refunds, support volume, downstream retention.