Basis Lab

Running an Online Experiment

When a Test Is the Wrong Tool

Last timeTesting Many Things at Once

Three situations defeat an ordinary split test: users who affect each other, effects that take months to appear, and outcomes too rare to measure in any run you can afford.

Three situations where the apparatus fails

Everything so far has assumed three things quietly. That one person

experiencing the change does not affect anybody else. That the effect appears

within the run. And that the outcome happens often enough to count.

Each of these fails in real situations, and the failure is not visible in the

output: the experiment produces a clean interval and a confident verdict,

which is wrong. This lesson is about recognising the three cases before

running anything.

When the groups affect each other

A delivery marketplace tests a change that makes couriers accept jobs faster.

Treated couriers accept faster, take more jobs, and earn more. The experiment

reports a large win.

The jobs the treated couriers took were jobs the control couriers would have

taken. The control group did worse because the treatment group existed, so the

measured difference is the real effect plus the amount the treatment took from

the control, which can be most of it. Ship the change to everybody and the

gain largely disappears, because there is nobody left to take from.

FIG 1Where the control group is affected by the treatment
what the treated user codirection of the errorunit that removes it
a two sided marketplace111
a social feed change212
a shared inventory111
a shared queue or suppor313
a change to prices in on421
The first column codes the shared thing: 1 is a limited supply of jobs or stock, 2 is other people attention, 3 is a limited amount of staff time, 4 is the market price itself. The error direction is coded 1 for overstated and 2 for either direction. The repairing unit is coded 1 for whole markets or cities, 2 for clusters of connected people, 3 for whole time periods. Every row has a repair and every repair costs a great deal of statistical precision, because the number of independent units falls from millions of people to a handful of cities.

The repair is to randomise something large enough to contain the interference.

Whole cities, whole markets, clusters of socially connected people, or whole

time periods alternating between the two versions. The arithmetic of the third

lesson still applies, with the number of units now being the number of cities

rather than the number of people, which usually means an effect has to be

several times larger before it can be detected.

Time periods are the most practical version for a marketplace. Run the old

version for half an hour, the new one for the next half hour, and alternate

for weeks. Every period sees the whole market, so there is no interference,

and the comparison is between periods rather than between people. The cost is

that periods differ from one another in ways people do not, so a large number

of them is needed.

The lesson stops here

6 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 Onceopening only
  7. 07When a Test Is the Wrong Toolyou are here
  8. 08What to Do With the Answeropening only

Read alongside