Feature Store Confidence Interval Calculator

1. Define one pass rule
Use a consistent binary condition, such as “feature returned within freshness SLA” or “schema validation passed.”

2. Enter the evaluated total
Provide the number of records or requests checked under that rule.

3. Enter passing observations
Provide the subset that satisfied the rule.

4. Select confidence
Choose the reporting confidence level used for the interval.

5. Interpret rate and range together
Use the interval width to judge how precisely the current sample supports the displayed pass rate.

p = passing observations / evaluated observationsSE = sqrt(p × (1 − p) / n)Confidence interval = p ± z × SE

Where:

• p = observed feature-store pass proportion
• n = number of evaluated observations
• SE = standard error
• z = selected normal critical value

Assumptions: The calculation uses a normal approximation and treats observations as independent Bernoulli outcomes. Repeated events from the same entity may reduce effective independence.

What the result means

The interval gives an approximate range of proportions compatible with the sampled feature-store observations under the model assumptions.

If observations are clustered by entity, region, or time window, consider a method that accounts for that dependence instead of treating every row as independent.

Given: 2,400 evaluated feature requests, 2,292 passing the quality rule, 95% confidence.

Calculation: p = 2,292 / 2,400 = 0.955. SE = √(0.955 × 0.045 / 2,400) ≈ 0.00423. Margin ≈ 1.959964 × 0.00423 = 0.00829.

Result: 95.50% observed pass rate with an approximate 95% interval of 94.67% to 96.33%.

Interpretation: The sample supports a high pass rate, while the interval shows the precision of that estimate rather than presenting 95.5% as exact.