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Sequential Probability Ratio Test.

Observe a coin stream and stop only when a predeclared likelihood-ratio boundary is crossed.

Interactive experimentintuitiveField note ·
INTERACTIVE EXPERIMENT / M13

Sequential Probability Ratio Test

Observe a coin stream and stop only when a predeclared likelihood-ratio boundary is crossed.

TEST THE MECHANISM

Compare only two coin models: H₀ has heads probability 0.4 and H₁ has 0.6. The likelihood ratio is updated after every flip, with pre-specified boundaries 1/9 and 9.

Observed flips
0
Log likelihood ratio
0.000
Likelihood ratio
1.000
Decision
Continue sampling

This Wald-style sequential probability ratio test uses nominal α=β=0.1 boundaries; actual error probabilities depend on overshoot and the specified simple hypotheses. It is not permission to repeatedly apply an unchanged fixed-sample p-value threshold. The 200-flip display cap is an inconclusive stop.

Only two simple Bernoulli hypotheses are compared. Discrete overshoot affects nominal errors; 200 observations without a crossing is inconclusive.

THE SHORT VERSION

Sequential Probability Ratio Test, explained.

The sequential probability ratio test compares cumulative evidence for two specified hypotheses while permitting a data-dependent stopping time.

01 / THE MECHANISM

Why it happens

The coin hypotheses are p = 0.4 and p = 0.6. Each observation adds or subtracts log(1.5); boundaries are ±log(9), corresponding to nominal error targets 0.1 with discrete overshoot.

The sequential probability ratio test compares cumulative evidence for two specified hypotheses while permitting a data-dependent stopping time.

Read the result

Read the accumulated log likelihood ratio against the declared boundaries. A capped unresolved stream remains inconclusive.

02 / FOLLOW IT THROUGH

A worked example

A coin stream with a planned stopping rule

  1. Choose the generating coin before observing the sequence under hypotheses 0.4 and 0.6.

  2. Observe outcomes and add or subtract log 1.5 until the first crossing of a log-nine boundary.

  3. Interpret the selected hypothesis as a model-dependent decision, retaining nominal error and overshoot limitations.

OPTIONAL DEEPER DETAILGo deeper: inside the model

Inside this model

The coin hypotheses are p = 0.4 and p = 0.6. Each observation adds or subtracts log(1.5); boundaries are ±log(9), corresponding to nominal error targets 0.1 with discrete overshoot.

03 / BEYOND THE EXPERIMENT

Where this idea is useful

A practical use

Use a stopping rule designed for sequential evidence rather than repeatedly applying a fixed-sample significance threshold.

CHECK YOUR INTUITION

A common misconception

THE TEMPTING CONCLUSION

“Repeatedly looking at any p-value preserves its fixed-sample error guarantee.”

THE MORE USEFUL DISTINCTION

Optional stopping needs an appropriate design. This board uses likelihood-ratio boundaries intended for sequential observations.

What this explanation leaves out

  • Only two simple Bernoulli hypotheses are compared. Discrete overshoot affects nominal errors; 200 observations without a crossing is inconclusive.
ONE MORE QUESTION

What happens if neither boundary is crossed?

After the display cap of two hundred observations, the result is inconclusive; the interface does not silently choose a hypothesis.

TAKE THE IDEA WITH YOU

Was the stopping rule chosen before the evidence started arriving?

Associated thinkers

Associated withAbraham Wald ↗

Associations marked provisional are awaiting source review.

Further reading

Explore the original research or the teaching reference behind this experiment.