Peeking is one of the most common ways that otherwise well-run tests produce false wins.
It feels harmless, because looking at results does not change them.
The harm comes from the decision rule: stopping as soon as the result looks good.
A p-value wanders up and down as data arrives, even when there is no real effect.
Over many looks it is likely to dip below 0.05 at some point, purely by chance.
A test that stops at the first dip is therefore much more likely to declare a false win than one that waits.
The more often the team looks, the higher the real false alarm rate climbs.
Peeking also exaggerates real effects, because tests tend to stop at a lucky high point.
The simplest fix is to set the sample size in advance and analyse once, at the end.
Sequential testing methods allow a planned number of looks by spreading the false alarm budget across them.
Some methods, such as always-valid p-values, allow a look at any time without inflating false alarms.
Switching to a different style of statistics does not automatically fix peeking, since stopping rules can bias any method.
Looking early is still useful for spotting broken tests, such as an empty treatment group or a crash.
In practice, teams agree that early looks are for safety only, never for declaring a winner.
The usual mistake is believing that a result which crossed 0.05 is significant, regardless of how many looks it took.