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tied-rank comparison matrix · scored comparison matrix

Correlate Paired Ranks Without Pretending Ties Are Distinct Observations

Tied values should share their occupied rank positions, not acquire an order from the spreadsheet row. Show both rank columns before describing monotonic association.

1 · Observation code | X value | Y value

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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.

Data note: This tied-rank comparison matrix calculates in the tab from Observation code | X value | Y value. No input-collection endpoint, AI request or file upload is built into it. Drafts may be saved locally; explicit input-state links and optional external formatting can disclose the records. Use non-sensitive codes and clear the draft when finished.

Perspective: Before: tied measurements acquired arbitrary positions or a misleading shortcut. After: average occupied ranks and the correlation denominator show exactly what monotonic association was computed.

2 · Read the tied-rank comparison matrix

Descriptive paired-rank exercise only: no p-value, causal conclusion or population guarantee. Verify pairing and measurement precision, and consult a qualified teacher or statistician before inferential or consequential use.

Optional filing controls are a local prototype.

Checkout is unavailable. The reading above is complete; print, CSV and five local summaries are optional enhancements, not hidden answers.

Before using the tied-rank comparison matrix

Each row is one paired observation. X and Y can use different measurement units, but the pairing must be genuine and both values must be finite plain decimals. The method ranks each variable ascending, assigning equal values their average occupied positions, then calculates Pearson correlation on those ranks. It reports only a descriptive coefficient: no p-value, confidence interval, causal explanation or sample-size guarantee is produced. The paired-count setting is your reminder to inspect a small set, not a statistical recommendation. Row order does not change the coefficient, while changing which X is paired with which Y legitimately does.

Boundary and sources

Descriptive paired-rank exercise only: no p-value, causal conclusion or population guarantee. Verify pairing and measurement precision, and consult a qualified teacher or statistician before inferential or consequential use.

Mechanism: competence-autonomy-loop

Optional AI formatting, never the calculation

Manual entry completes this tied-rank comparison matrix 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 tied-rank comparison matrix. Return strict JSON shaped as {"rows": [{"label": "string", "cells": ["string", "string"]}], "setting": "string"}. The columns are Observation code | X value | Y value; the setting is Paired-count review floor (not a significance rule). 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.