false precision

Numerical basis controls the test

Assessment must distinguish exact or defined quantities from measurements, estimates and forecasts before questioning their precision.

1. What quantity, population, time and unit does this numerical claim refer to? definition
2. Is the value exact by counting or definition, or is it inferred from uncertain observations or assumptions? boundary

Displayed detail can imply distinguishability

Decimal places, significant digits and precise endpoints can suggest that nearby values are meaningfully distinguishable, depending on accompanying qualifications.

1. What is the smallest increment shown, and how is it affected by the unit or notation? measurement
2. Does the surrounding text present the number as approximate, exact, provisional or merely an intermediate computational value? boundary

Input detail needs a source

The assessment should identify how input values were observed, recorded and rounded, including limitations that displayed input digits do not reveal.

1. Which source records establish the input values, their resolution and any prior rounding? provenance
2. Which limitations, such as sampling, coverage, calibration or recording error, affect the precision those inputs can support? measurement

Support must match the claim

Any uncertainty estimate used to justify reported precision must apply to the claimed quantity and identify which uncertainty sources it includes.

1. What uncertainty estimate, bound or reporting rule supports the chosen precision, and how was it obtained? provenance
2. Does that support cover the claimed quantity and relevant uncertainty sources, or only a narrower component? boundary
3. If support is unavailable, what evidence would distinguish unjustified precision from adequately supported but undocumented precision? action

Computed digits do not create evidence

Arithmetic or an exact conversion factor can generate additional digits while leaving the underlying real-world uncertainty unresolved.

1. Which calculations or unit conversions introduced digits absent from the original evidence? provenance
2. How were input uncertainty, dependencies and prior rounding accounted for before selecting output precision? measurement

Aggregate precision requires method-specific support

Aggregation may support finer estimates under appropriate conditions, but sample size or software output alone does not establish how much precision is warranted.

1. What assumptions about independence, bias, model fit or population coverage justify the reported aggregate precision? provenance
2. How much do plausible input revisions or model choices change the reported final digits? measurement

Visible differences may not support an order

A numerical ranking may imply more stable distinctions than the uncertainty of the compared values permits.

1. Are comparisons based on differences smaller than the evidence can reliably distinguish? measurement
2. Does the comparison account for shared uncertainty or dependence between the values? boundary
3. Should the presentation use ties, ranges or an explicit statement that the ordering is unresolved? action

Precision can overstate decision certainty

A finely stated value near a threshold may suggest a settled classification even when supported alternatives fall on both sides.

1. Which audience or downstream system uses this number, and what decision threshold or rule does it apply? boundary
2. Do values consistent with the available evidence produce different classifications or actions? measurement

A finding requires an explicit mismatch

A supported assessment identifies what precision was conveyed, what support was available and why the two conflict; missing documentation can leave the assessment unresolved.

1. What specific evidential limitation contradicts the precision conveyed by this claim? measurement
2. Could exactness, a valid reporting convention, accompanying uncertainty or a stated computational purpose justify the displayed detail? boundary

Repair must address the mechanism

Possible repairs include supported rounding, uncertainty disclosure, revised intervals or qualified comparisons; removing digits alone cannot resolve every form of false precision.

1. Which correction addresses the identified mismatch, and what evidence supports the replacement representation? action
2. After correction, do the number, interval, wording and downstream comparisons convey mutually consistent precision? measurement
3. How will the original claim, correction rationale and remaining uncertainty be retained for review? provenance

Check these first

Recalled without web access and unsourced; every item is a lead to verify.

1. Which of these check these first hold for the sense of false precision this model covers, and on what evidence? provenance

Standards and regulation

Recalled without web access and unsourced; every item is a lead to verify.

1. Which of these standards and regulation hold for the sense of false precision this model covers, and on what evidence? provenance

Real-world use

Recalled without web access and unsourced; every item is a lead to verify.

1. Which of these real-world use hold for the sense of false precision this model covers, and on what evidence? provenance

Failure modes and hazards

Recalled without web access and unsourced; every item is a lead to verify.

1. Which of these failure modes and hazards hold for the sense of false precision this model covers, and on what evidence? provenance

Neighbouring kinds and how to tell them apart

Recalled without web access and unsourced; every item is a lead to verify.

1. Which of these neighbouring kinds and how to tell them apart hold for the sense of false precision this model covers, and on what evidence? provenance