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Is the new supplier better? Did the new method shorten the cycle? Did the poka-yoke cut scrap? Paste the measurements for A and B and see whether the difference is real, whether it matters in practice, whether the sample was large enough and what it is worth per year.
Pre-sizing and estimating tool: results do not replace design, calculations or reports signed by a licensed professional, nor verification against the applicable codes and standards.
Dot = estimate; bar = confidence interval; red line = H₀; green band = difference that does not matter in practice.
Difference B − A applied to the annual volume entered.
Dots = each measurement; box = middle 50% and median; diamond with bar = estimate and CI; dashed lines = H₀ or specification.
Distribution of each sample’s values, with each mean.
Chance of detecting each true difference with the current sample (solid) and with the sample that gives 80% at the difference that matters (dashed).
How the test statistic is distributed if H₀ is true; the red area is the P-value.
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Use the "Compare A × B" mode: paste the measurements for A and B and say which is better (higher or lower). The page picks the test, shows the difference B − A with its confidence interval and tells you whether it is real, large enough to matter and whether the sample was big enough. In the cycle-time example, the new method is about 2.2 s faster and the whole interval lies beyond 1 s, so the improvement is real and relevant.
That a difference as large as the one observed would be rare (under 5% chance) if A and B were the same. It does not say whether the difference is large: with huge samples, tiny differences become significant. That is why the page also shows the confidence interval and the band of the difference that matters.
When the same part, lot or operator appears in both conditions, such as the same part measured on two gauges or the same lot before and after a treatment. Pairing removes part-to-part variation and often detects differences the 2-sample test misses. Different parts in A and B: use the 2-sample test.
Only if the whole confidence interval fits inside the green band of the difference that matters. If the interval is wide, the result is inconclusive: the sample was too small. The power curve shows the chance the test had of seeing the difference and how many more samples are needed for 80%.
To compare two proportions defective (before × after, shift × shift), use 2 proportions: the page runs the Z test and Fisher’s exact test, which holds even with few defects. For counts of defects per part, failures per month or stops per shift, use the Poisson rates.
To compare the spread (stability) of two processes without assuming normal data. The F and chi-square tests are exact only for normal data and can be badly off with heavy tails. A process B with the same mean but a smaller standard deviation is also an improvement: fewer parts out of specification.
Because each look is a new chance of a false alarm. Looking 5 times at 5%, the real chance of “finding” a difference that does not exist reaches about 14%. Set the sample size beforehand, or enter how many times you looked: the page shows the real α and the corrected p threshold for each look.
Important notice: Indus Metrics tools produce estimates and pre-sizing results from the data you enter and from published engineering methods. They do not replace design, calculations or reports signed by a licensed professional engineer (PE), nor verification against the applicable codes, standards and regulations, such as OSHA rules and the ASME codes. Material and fuel properties vary: check manufacturer data or your own process analyses before deciding. By using this site you agree to the Terms of Use.
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