Which sequence best describes a hypothesis-driven experimentation process?

Which sequence best describes a hypothesis-driven experimentation process?

• A. Start with a testable hypothesis, run a controlled experiment, measure outcomes, decide to pivot, persevere, or pivot.
• B. Conduct experiments without a hypothesis and make decisions after end.
• C. Build a fully featured product first, then test with users.
• D. Use gut instinct to decide the next step after experiments.

Answer: (A)

Starting with a testable hypothesis sets up a clear question and a way to measure what matters. In a hypothesis-driven experimentation process you specify an assumption about how a change will affect user behavior or outcomes, then design a controlled experiment to isolate the variable and gather data that speaks to that assumption. After collecting and analyzing the results, you decide the next step: pivot if the hypothesis is not supported, persevere if it is, or adjust and re-test to refine your understanding. This loop creates learning that is tied to measurable evidence, helping you move forward efficiently and with confidence. Other approaches miss this focused, data-driven cycle: testing without a guiding hypothesis leaves you without a directional question; building a fully featured product before validating assumptions wastes time and money; and deciding next steps based on gut feeling ignores what the data actually shows.

Starting with a testable hypothesis sets up a clear question and a way to measure what matters. In a hypothesis-driven experimentation process you specify an assumption about how a change will affect user behavior or outcomes, then design a controlled experiment to isolate the variable and gather data that speaks to that assumption. After collecting and analyzing the results, you decide the next step: pivot if the hypothesis is not supported, persevere if it is, or adjust and re-test to refine your understanding. This loop creates learning that is tied to measurable evidence, helping you move forward efficiently and with confidence. Other approaches miss this focused, data-driven cycle: testing without a guiding hypothesis leaves you without a directional question; building a fully featured product before validating assumptions wastes time and money; and deciding next steps based on gut feeling ignores what the data actually shows.