Margin of error for a sample size
API · /samplesize-api
Sample Size API
Survey and poll sample-size planning as an API, computed locally and deterministically. The proportion endpoint computes the number of respondents needed to estimate a proportion within a target margin of error at a chosen confidence level, n = z²·p(1−p)/E², defaulting to the worst-case p = 0.5 that maximises the required size, with an optional finite-population correction n/(1 + (n−1)/N) for a known population — the classic ±5 % margin at 95 % confidence needs 385 responses, ±3 % needs 1 068, and capping the population at 1 000 cuts the ±5 % requirement to 278. The mean endpoint sizes a sample for estimating a mean to within a margin of error from the standard deviation, n = (z·σ/E)². The margin endpoint inverts the relationship, returning the margin of error a given sample size actually achieves. The critical z-value is computed from the confidence level with a high-accuracy inverse-normal so any confidence works, not just the textbook 90/95/99 %. Margins, proportions and confidence are decimals (0.05, 0.5, 0.95). Everything is computed locally and deterministically, so it is instant and private. Ideal for market-research, polling, UX-research, survey-platform, product-analytics and statistics-education app developers, study-planning and sample-size tools, and research software. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is sample-size planning with the normal approximation; for A/B-test significance use an A/B-test API and for descriptive statistics a statistics API.
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- Sondaggi del server · 24 ore su 24
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- Chiamate totali
- 20
- ultimi 7 giorni
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Free
Gratis
- 6,200 chiamate/mese
- 2 richieste/secondo
- Tetto rigido (429 sopra la quota, nessuna eccedenza)
- 6,200 llamadas/mes
- 2 solicitudes/seg
- Proporción + media + margen de error
- Sin tarjeta de crédito
Starter
€5.60 /mese
- 62,000 chiamate/mese
- 6 richieste/secondo
- Tetto rigido (429 sopra la quota, nessuna eccedenza)
- 62,000 calls/month
- 6 req/sec
- Any confidence level, finite population
- Email support
Pro
€15.80 /mese
- 280,000 chiamate/mese
- 15 richieste/secondo
- Tetto rigido (429 sopra la quota, nessuna eccedenza)
- 280,000 calls/month
- 15 req/sec
- Survey-platform & research pipelines
- Priority support
Mega
€49.50 /mese
- 1,550,000 chiamate/mese
- 40 richieste/secondo
- Tetto rigido (429 sopra la quota, nessuna eccedenza)
- 1,550,000 llamadas/mes
- 40 req/seg
- Escala de plataforma
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Correlato APIs
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Statistical Inference API
Inferential-statistics maths as an API, computed locally and deterministically. The samplesize endpoint computes how many respondents a survey or experiment needs for a proportion, n = Z²·p(1−p)/E², from a confidence level and a margin of error (using p = 0.5 for the most conservative size), with a finite-population correction when the population is known. The confidence endpoint builds a confidence interval for a mean (estimate ± Z·σ/√n) or a proportion (p ± Z·√(p(1−p)/n)), returning the standard error, margin of error and the lower and upper bounds. The ztest endpoint runs a one-sample z-test, z = (x̄ − μ₀)/(σ/√n), and returns the z-score, the one- or two-tailed p-value and whether the result is significant at the chosen alpha. The z-scores come from an exact inverse-normal and the p-values from the normal CDF. Everything is computed locally and deterministically, so it is instant and private. Ideal for A/B-testing, survey, research and analytics app developers, experiment dashboards and data-science tools, and education. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is inferential statistics; for descriptive statistics use a statistics API and for probability distributions use a probability API.
api.oanor.com/inference-api
Climbing Fall API
Rock-climbing fall maths as an API, computed locally and deterministically — the safety numbers behind a lead fall, from the harshness of the catch to whether you hit the deck. The fall-factor endpoint gives the fall factor, distance fallen ÷ rope paid out, from 0 to a maximum of 2: it, not the absolute distance, decides how hard the catch is, so 4 metres on 2 metres of rope is a brutal factor-2 onto the anchor while the same fall on 10 metres of rope is a mild 0.4. The impact-force endpoint gives the peak force the rope transmits from the spring model F = mg + √((mg)² + 2·mg·k·f), where k is the rope modulus (~20 kN for a dynamic single rope) and f the fall factor — so an 80 kg climber on a factor-1 fall feels about 6.4 kN, and the top runner sees roughly 1.66× that from the pulley effect. The ground-fall endpoint adds it up: total drop = twice the height above the last piece, plus slack, plus the rope's stretch, and tells you whether that clears the ground or a ledge. Everything is computed locally and deterministically, so it is instant and private. Ideal for climbing apps, gym and guiding tools, route-planning and education sites, and gear calculators. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 compute endpoints. Educational estimates — not a substitute for instruction and judgement.
api.oanor.com/climbing-api
Plumbing Code API
Plumbing-code sizing maths as an API, computed locally and deterministically — the fixture-unit and pipe-sizing numbers a plumber, designer or inspector runs from the code book. The dfu endpoint totals drainage fixture units for a set of fixtures (IPC Table 709.1): pass a list like toilet:2,lavatory:3,shower:1,kitchen_sink:1 and it weights each by its discharge — a toilet is 3, a lavatory 1, a tub or shower 2 — for a total of 13, with a grouped full bathroom counting as 6 rather than the sum of its parts. The pipe-size endpoint gives the minimum building-drain size for a DFU load at a slope (IPC Table 710.1(1)): the smallest pipe whose capacity meets the load, so 50 DFU at a quarter-inch-per-foot fall needs a 4-inch drain, with the reminder that any drain carrying a water closet is a 3-inch minimum. The supply-gpm endpoint reads probable peak water demand off the Hunter curve: diversity means 100 supply fixture units draws only about 54 GPM, not the sum of every fixture running at once — the number you size the water service against. Everything is computed locally and deterministically, so it is instant and private. Ideal for plumbing-design and estimating apps, code-check and permit tools, MEP-engineering calculators, and trade-school aids. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 compute endpoints. Based on the IPC — verify against the code adopted in your jurisdiction.
api.oanor.com/plumbing-api
Pool Heating API
Swimming-pool and spa heating maths as an API, computed locally and deterministically — the thermodynamics a pool owner, builder or service tech sizes a heater and budgets a heat-up with. The heat-time endpoint gives the hours to warm a body of water: energy = gallons × 8.34 lb/gal × the temperature rise in °F (that many BTU), divided by the heater's BTU/hr output — raising 20,000 gallons by 10 °F is 1,668,000 BTU, about 4.2 hours on a 400,000 BTU/hr gas heater before surface losses. The heater-size endpoint inverts it: the output you need to hit a temperature rise within a target time, so the same job in 24 hours wants only about 69,500 BTU/hr. The heat-pump endpoint gives a heat pump's electricity and cost — kWh = thermal BTU ÷ 3412 ÷ the COP (5–6 for pool units in mild weather) — so that 1,668,000 BTU costs about 89 kWh at a COP of 5.5, a fraction of resistance heat. Pass the temperature rise directly, or a current and target temperature. Everything is computed locally and deterministically, so it is instant and private. Ideal for pool-builder and service apps, heater-sizing and quote tools, spa and hot-tub calculators, and energy-comparison sites. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 compute endpoints. Ideal figures — add for surface and wind losses. For pool chemistry use a pool-chemistry API.
api.oanor.com/poolheat-api
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curl https://api.oanor.com/samplesize-api/SOME_PATH \
-H "x-oanor-key: oanor_test_..."
const res = await fetch("https://api.oanor.com/samplesize-api/SOME_PATH", {
headers: { "x-oanor-key": "oanor_test_..." }
});
const data = await res.json();
$ch = curl_init("https://api.oanor.com/samplesize-api/SOME_PATH");
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, ["x-oanor-key: oanor_test_..."]);
$response = curl_exec($ch);
import requests
r = requests.get(
"https://api.oanor.com/samplesize-api/SOME_PATH",
headers={"x-oanor-key": "oanor_test_..."},
)
print(r.json())
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