After five days of a store listing experiment, one of your variants outperforms the current version. Why should you continue to run your experiment for at least seven days even though you think that you have enough data now?
- You should run as many variants as possible before you make a decision.
- You must run the experiment for at least seven days to see a recommendation in the results column.
- You need to see an accurate reflection of weekday and weekend behavior.
- You must run the experiment for at least 7 days to see a recommendation in the metrics-used column.
Here’s why:
- User behavior can vary: People use apps differently on weekdays versus weekends. For example, a gaming app might see more engagement on weekends, while a productivity app might see more use during weekdays.
- Short experiments can be misleading: If you end the experiment after only five days, you might not capture a full week of user behavior. This could skew the results and lead you to make a decision based on incomplete data.
- Seven days is a good starting point: Running the experiment for at least seven days helps ensure that you capture a more representative sample of user behavior across the entire week.
The other options are incorrect:
- You should run as many variants as possible before you make a decision. While testing multiple variants can be helpful, it’s not the reason to continue the experiment for a full week.
- You must run the experiment for at least seven days to see a recommendation in the results column. The experiment will provide results even if it runs for less than seven days, but those results might not be reliable.
- You must run the experiment for at least 7 days to see a recommendation in the metrics-used column. The metrics used in the experiment are determined by you, not the experiment duration.
The main chapter where the reference to the correct answer can be found is “Optimize your store listing with experiments”. The section on “Set up experiments” mentions that experiments should run for at least seven days to get enough data for reliable results. This implies that a shorter duration might not capture the full range of user behavior, including weekday and weekend variations.