Basis Lab

Running an Online Experiment

Testing Many Things at Once

Last timeWhy You Must Not Peek

Watching twenty measures or running five variants multiplies the chances for noise to cross the line, so a win on the eleventh measure is more likely to be noise than a finding.

The same arithmetic, pointed sideways

The previous lesson was one measure looked at many times. This one is one look

at many measures, and the arithmetic is identical.

Twenty measures on the dashboard, none of them actually changed. The chance

that at least one of them crosses the usual threshold is about two thirds. The

team finds that one, writes it up, and has a finding.

FIG 1The chance of at least one crossing, among many measures
the threshold applied to each measure
how many measures, variants or slices were examined
the chance that at least one of them crosses
The same expression as the peeking lesson, with measures in place of looks. The important part is what counts towards m. Every measure on the dashboard counts. Every variant counts. Every slice of users counts. Every version of the analysis counts, including the one with the outliers removed. The number that matters is how many comparisons could have produced the reported finding, not how many are in the write up.
FIG 2What counts towards the number of comparisons
how many, typicallycounted by the teamcounted by the arithmeti
measures on the dashboar20120
variants against one con414
slices of the audience12012
versions of the analysis303
weeks the run could have303
The middle column codes whether teams normally count it, as 1 for yes and 0 for no. Multiply the last column out and a team looking at twenty measures, four variants and a dozen slices has thousands of opportunities for noise to produce something striking. The three rows coded zero are the ones that do the damage, because they feel like looking more carefully rather than like testing more hypotheses.

Correcting for the family

The simplest repair is to divide the allowance. If twenty measures are being

examined and the overall false alarm rate should stay at one in twenty, then

each measure gets one four hundredth.

FIG 3The threshold for each measure
the false alarm rate wanted across the whole set
the number of measures being examined
the threshold each individual measure has to cross
Crude, conservative, and completely reliable. It costs traffic: a threshold of one in four hundred instead of one in twenty needs a difference about forty per cent larger to cross, which is roughly twice the traffic to detect the same effect. That cost is the real reason teams avoid the correction, and the honest response is to examine fewer measures rather than to drop the correction.

A less conservative alternative controls the share of the reported findings

that are false, rather than the chance of any false finding at all. Instead of

one flat tight threshold, the measures are sorted by how extreme they are and

the threshold is tightened progressively down the list. When a team is

genuinely screening many measures to see which are worth following up, this is

the better tool, because it keeps the share of nonsense among the findings

under control without demanding that every single one be beyond doubt.

The lesson stops here

2 more paragraphs to go

You have read the opening. The rest of the argument, the problems that check whether it landed, and the lines worth keeping at the end all come with a plan.

The first lesson of every course in the library reads the whole way through, free, so you can see exactly what the rest of them are.

See the planThe contents

This is the reading half

Starting the course gives you your own copy of it. Every idea on every page has problems standing under it, marked with a reason rather than a tick, and any sentence you do not believe can be opened and argued with. None of that can happen on a page nobody owns.

The contents

The rest of this course

  1. 01Saying What Would Count as Better
  2. 02Splitting the Traffic Fairlyopening only
  3. 03How Much Traffic It Will Takeopening only
  4. 04Reading the Resultopening only
  5. 05Why You Must Not Peekopening only
  6. 06Testing Many Things at Onceyou are here
  7. 07When a Test Is the Wrong Toolopening only
  8. 08What to Do With the Answeropening only

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