The Season Wasn't Long Enough

Opening Scene

A farmer who digs up a plant three weeks into a growing season to check on its progress isn’t just being impatient — they’re disturbing the very trial they’re trying to observe, and drawing conclusions from a plant that hasn’t finished growing yet. A trial that gets judged before the season is over, or that gets ended the moment an early result looks promising, risks the exact same mistake: mistaking an incomplete picture for a finished one.

In Plain English

Test duration and the discipline of not “peeking” at results before a trial is complete are both essential to a trustworthy experiment. Checking results repeatedly and stopping the moment they look statistically significant — a practice sometimes called p-hacking through early stopping — dramatically inflates the chance of a false positive, since random early fluctuations are far more likely to look significant than the trial’s true, settled result.

The Old Way

Before formal guidance on test duration and peeking existed, this exact mistake was common:

A researcher checking results daily and stopping the moment a p-value crossed a threshold, without accounting for how much this inflates the false-positive rate.
A business ending a test early because early numbers looked good, without waiting for the full planned duration to confirm the pattern held.
A farmer judging a crop’s success partway through the season, before natural variation had a chance to settle into a stable, trustworthy pattern.

In each case, an early, incomplete look at a noisy process was mistaken for a final, trustworthy result.

What’s Changing (and Why AI Is the Reason)

Formal sequential testing methods, covered in more depth in Article 15, have been developed specifically to allow legitimate early stopping without inflating false positives, using statistically rigorous adjustments rather than ad hoc peeking.
Experimentation platforms increasingly enforce a pre-registered test duration, connecting directly to the sample size calculations from Article 7, discouraging the temptation to stop early just because an interim result looks favorable.
Growing awareness of novelty effects — where users respond differently to a new feature simply because it’s new, an effect that fades over time — has reinforced the importance of running tests long enough to see past this initial, temporary reaction.

The Metaphor, Fully Extended

The Field Trial	Test Duration Concept
Digging up a plant partway through the season to check progress	Peeking at results before a test is complete
Judging a crop’s success before the season has run its full course	Stopping a test early because an interim result looks favorable
A season’s natural variation settling into a stable, trustworthy pattern	A test’s noisy early results settling into a stable, trustworthy pattern
Waiting out the full growing season before drawing a conclusion	Running a test for its full planned duration before drawing a conclusion

For Beginners: What to Actually Do

Commit to a test’s planned duration and sample size, calculated in advance using the methods from Article 7, before looking at any results.
Learn what a novelty effect is and how to distinguish it from a treatment’s genuine, lasting effect.
If you genuinely need to check results early, learn the formal sequential testing methods from Article 15 rather than informally peeking.

For Practitioners and Leaders: The Deeper Layer

Build organizational discipline around pre-registering test duration and resisting the urge to stop early based on favorable interim results.
Invest in formal sequential testing infrastructure specifically for cases where genuinely faster decisions are needed, rather than allowing informal peeking as a workaround.
Track novelty effects explicitly for major feature launches, distinguishing an initial spike in engagement from a lasting behavioral change.

Quick Recap

Peeking at results before a trial is complete and stopping early inflates the risk of a false positive conclusion.
Novelty effects can make an early result misleadingly positive before settling into a more accurate, lasting pattern.
Formal sequential testing methods allow legitimate early stopping without this inflated risk.
Committing to a pre-registered test duration is essential discipline, not unnecessary caution.

Where This Fits in the Series

Article 8 covered the risk of judging a trial too early. Article 9 covers a different threat to a trial’s validity: something going wrong inside the data itself.