Expose Respondent Mix Before Applying Supplied Population Weights — Free
A heavily responding small district can dominate the raw percentage. Compare the respondent mix with a supplied population frame, while keeping the strong assumptions behind any reweighted estimate in plain view.
The proof surface
Theme-incidence tools count what received responses mention. This chart instead reconciles mutually exclusive population strata and respondent counts for one coded binary question. It shows raw and reweighted denominators without claiming the survey represents nonrespondents.
InputNonoverlapping stratum | eligible population | respondents | affirmative respondents; population-to-respondent weight review mark
Rare deviceTheme-incidence tools count what received responses mention. This chart instead reconciles mutually exclusive population strata and respondent counts for one coded binary question. It shows raw and reweighted denominators without claiming the survey represents nonrespondents.
Output artifactAffirmative share under supplied population weights with row-by-row context
Cost$0 local calculation · no card, paid key, subscription or signup · proposed filing price is not for sale
Sample, not your facts: Illustrative inputs: East district | 800 | 80 | 48; West district | 200 | 100 | 20. Population-to-respondent weight review mark = 5. Affirmative share under supplied population weights: 52 % population-weighted affirmative. All records are invented.
Before using the response-mix chart
Use one binary question with a documented affirmative coding rule. Strata must be nonoverlapping and cover the population frame you intend to describe; the calculator cannot verify a sampling frame or deduplicate respondents. Population, respondents and affirmative respondents are whole units under the same person or household convention. Each included stratum needs at least one respondent, because no within-stratum rate can be estimated from an empty group. The population-to-respondent review mark highlights large expansion weights but never clips or changes them. Reweighting only addresses the entered stratum mix; it does not solve self-selection, question bias, missing strata, measurement error or unequal response propensity within a stratum.
Why the flat version breaks
Treating weights as new responses
A modeled contribution such as 480 from eighty respondents is an expansion under an assumption, not 480 observed affirmative answers. Keep the response counts visible and label the estimate. Reweighting changes the denominator mix; it does not manufacture evidence from people who did not respond.
Dropping a stratum with no respondents
Removing an empty positive-population stratum changes the target population and conceals a coverage gap. The tool rejects that missing rate. Obtain appropriate data or describe the estimate as unavailable; do not assign zero affirmation or borrow another stratum's rate without a separately justified method.
Calling the weighted percentage representative
Correcting entered group proportions cannot fix within-group self-selection or biased questions. The review mark only highlights expansion weights. A qualified analyst must assess the design and limitations before a public interpretation; the chart's precise arithmetic is not evidence of statistical validity.
How to work the response-mix chart
Document the frame and coding
Obtain population counts from a dated, suitable source and define mutually exclusive strata. Record whether the unit is a person or household. Confirm how affirmative answers, missing answers and ineligible responses were coded. Keep respondent identities out of the app: aggregate counts and neutral stratum labels are the only inputs needed.
Check each nested count
Population must be positive, respondents must be positive and no greater than population, and affirmative respondents must lie from zero through respondents. A zero-response stratum is a visible limitation, not an opportunity to substitute the overall raw rate. Collect more evidence or report the estimate unavailable rather than removing that stratum silently.
Keep raw and weighted shares side by side
Within each stratum, divide affirmative respondents by respondents. Multiply that fraction by the supplied population, add the modeled contributions and divide by total population. Also show total affirmative respondents divided by total respondents as the raw share. The row's population/response weight explains why the two statistics can differ without changing any recorded answer.
State assumptions before a public conclusion
Ask a qualified survey analyst to review frame coverage, weighting and nonresponse before publishing a population claim. Present counts, dates, raw share and weighted share together. A reweighted estimate is not a referendum result or a confidence interval, and the app provides no mandate, policy recommendation or proof that unobserved opinions match observed ones.
What the response-mix chart separates
Question
Before
Inspect this instead
Treating weights as new responses
A modeled contribution such as 480 from eighty respondents is an expansion under an assumption, not 480 observed affirmative answers. Keep the response counts visible and label the estimate. Reweighting changes the denominator mix; it does not manufacture evidence from people who did not respond.
Check each nested count
Dropping a stratum with no respondents
Removing an empty positive-population stratum changes the target population and conceals a coverage gap. The tool rejects that missing rate. Obtain appropriate data or describe the estimate as unavailable; do not assign zero affirmation or borrow another stratum's rate without a separately justified method.
Keep raw and weighted shares side by side
Calling the weighted percentage representative
Correcting entered group proportions cannot fix within-group self-selection or biased questions. The review mark only highlights expansion weights. A qualified analyst must assess the design and limitations before a public interpretation; the chart's precise arithmetic is not evidence of statistical validity.
State assumptions before a public conclusion
This response-mix chart replaces a manual count or calculation, not source verification or the responsible person’s review.
Run it on the samples, right here
FIRST-LOAD
HYPOTHESIS / PROTOTYPE — checkout unavailable. Calculation is local. A draft is saved automatically in this browser profile when storage is available; Reset to sample clears it. Optional Pro history stores only five summaries and has its own deletion control. State links encode your inputs and can remain in browser history, clipboard or recipients’ records; share only non-sensitive rows. Optional external AI formatting leaves this device. The required site analytics beacon reports page activity; shared URLs contain encoded inputs. Do not treat an encoded URL as private. The calculator has no input-collection endpoint.
Descriptive weighting arithmetic only, not a representative-survey certificate, confidence interval or policy mandate. Ask a qualified survey analyst to review sampling-frame coverage, coding and nonresponse assumptions before publishing population conclusions.
Data note: The response-mix chart processes Nonoverlapping stratum | eligible population | respondents | affirmative respondents locally. Starter/sample selection and Run compute in this tab; no input is sent by the calculator. A local draft may be saved; explicit state-link sharing or optional external AI formatting can disclose inputs. Use non-sensitive labels.
Go deeper: the companion app files the same reading as a plotted bar chart sheet
The article demo above runs without limits. The companion app keeps a local history, exports the rows as CSV, prints the response-mix chart reading, and holds your drafts on this device — one complete free app run; the proposed $4 one-time filing layer is not for sale.
The response-mix chart answer stays complete for free. The proposed $4 one-time filing layer adds row CSV, print and five local reading summaries, not hidden answers. Checkout is unavailable; the article demo remains unlimited.
Descriptive weighting arithmetic only, not a representative-survey certificate, confidence interval or policy mandate. Ask a qualified survey analyst to review sampling-frame coverage, coding and nonresponse assumptions before publishing population conclusions.
What this is built on
Method: Within each stratum, divide affirmative respondents by respondents. Multiply that fraction by the supplied population, add the modeled contributions and divide by total population. Also show total affirmative respondents divided by total respondents as the raw share. The row's population/response weight explains why the two statistics can differ without changing any recorded answer.
All sample records, dates, quantities and labels are invented. No outside policy, contract, rate, clock offset, measurement or accessibility standard is represented as verified.
Google’s official pricing documentation, fetched 2026-10-01, says AI Studio is free in available regions. Optional formatting may require a Google account; manual local entry requires none. Limits can change and free-tier content may be used to improve products. Do not send private records.
Before: a raw response percentage hid an uneven respondent mix. After: observed counts and modeled population weights remain visibly different quantities.
Three worked readings, with different inputs
Sample A — typical inputs
East district | 800 | 80 | 48
West district | 200 | 100 | 20
East's observed affirmative share is 60%; West's is 20%. Weight those shares by their supplied populations: (800 × 0.6 + 200 × 0.2)/1,000 = 52%. The raw respondent percentage is 68/180 = 37.777778%. The difference comes from respondent mix, not new observations. East's weight is ten eligible units per respondent, beyond the illustrative review mark of five.
Sample B — changed plan
North district | 300 | 60 | 30
South district | 700 | 70 | 56
North contributes 300 × 0.5 = 150 modeled affirmative units; South contributes 700 × 0.8 = 560. Their total 710 divided by population 1,000 gives 71%. The raw respondent percentage is 86/130 = 66.153846%. Reweighting assumes respondents represent their own strata; it does not establish that assumption or correct within-stratum nonresponse bias.
All forty eligible units are reported as respondents, with ten affirmative, so raw and weighted shares are both 25%. A population-to-respondent weight of one needs no mix adjustment. This boundary still depends on correct population and response coding. A positive-population stratum with zero respondents would be rejected as unestimable instead of assigned a guessed affirmative rate.
Optional AI formatting, never the calculation
Manual entry completes this response-mix chart for free without signup. If available to you, the free AI Studio interface linked in the sources may format fictional or non-sensitive notes; external access may require an account. No API key or AI call is built into this tool. Free-tier content may be used to improve products. Review each cell and transcribe it to the labeled row schema; do not paste the JSON object into the row box.
Format only these fictional or non-sensitive notes for a response-mix chart. Return strict JSON shaped as {"rows": [{"label": "string", "cells": ["string", "string", "string"]}], "setting": "string"}. The columns are Nonoverlapping stratum | eligible population | respondents | affirmative respondents; the setting is Population-to-respondent weight review mark. Keep all supplied strings and quantities exactly; do not calculate, infer missing entries, invent dates or add advice. If any required value is missing, return an empty rows array and ask me for it separately. I will verify every cell against my source and manually transcribe rows using vertical bars before running the local calculator.
An AI response is not executed, fetched or trusted as a result. Missing values remain questions; the strict local parser checks the rows you actually enter.