data-outlier-finder

name	data-outlier-finder
description	Identifies unusual values, unexpected patterns, and potential stories hidden in a dataset by systematically checking for statistical outliers and contextual anomalies.
status	stable
category	data-journalism
subcategory	analysis
version	1
eval_score	4.2
tags	["data-journalism","analysis","outliers","anomalies","investigation"]

Data outlier finder

What this skill does

Identifies unusual values, unexpected patterns, and potential stories hidden in a dataset by systematically checking for statistical outliers and contextual anomalies.

When to use this skill

• You have a dataset and want to know where the stories are before you start reporting
• An editor asks "what's surprising in this data?" and you need a structured answer
• You are fact-checking a claim and want to verify whether the cited figure is genuinely unusual
• You received a data dump (FOI response, leaked spreadsheet, public database) and need to triage it for newsworthy patterns

What you need to provide

Required:

• The dataset or a representative sample (paste the data, describe the columns, or share summary statistics)
• What the data measures and where it comes from

Optional:

• What you expect to find (so the assistant can flag deviations from your assumptions)
• Known context that might explain outliers (e.g., "2020 data will be distorted by COVID")
• Whether you want statistical outliers only or also contextual anomalies (values that are technically valid but editorially surprising)

How the Assistant Approaches This

1. Identifies the key numeric columns and their expected ranges
2. Flags statistical outliers — values more than 2 standard deviations from the mean, or in the top/bottom 5% of the distribution
3. Checks for contextual anomalies — sudden changes between time periods, values that contradict known patterns, or categories that break expected rankings
4. For each outlier, states: what the value is, why it's unusual, and what reporting question it raises
5. Prioritizes findings by editorial potential — leads with the outliers most likely to be stories, not just noise

Output format

A numbered list of findings, ordered by editorial potential. Each finding includes:

• The value and where it appears in the dataset
• Why it's unusual (statistical context or comparison)
• The reporting question it raises — what a journalist should check next

Ends with a short summary: how many outliers were found, how many are likely noise vs. potential stories, and what additional data would help confirm the findings.

Quality criteria

• Each outlier is identified with its exact value, location in the dataset, and comparison point
• Statistical and contextual outliers are distinguished — the reader knows which is which
• Each finding includes a concrete reporting question, not just "this is unusual"
• Known distortions (COVID, policy changes, data collection breaks) are acknowledged, not flagged as discoveries
• Findings are prioritized by editorial potential, not listed in spreadsheet order

Example

Input

Dataset: Annual per-capita healthcare spending (USD, PPP-adjusted) for 30 OECD countries, 2018–2023. Source: OECD Health Statistics. I want to find anything surprising that could anchor a comparative healthcare story for a general-interest magazine.

Output

Finding 1: The United States remains a dramatic outlier at $12,555 per capita (2023) The US spends 2.3× the OECD median ($5,460). This gap has widened, not narrowed, since 2018 (+18% vs. OECD median +12%). The editorial angle isn't the outlier itself — that's well known — but the acceleration. The gap is growing faster than most readers assume. Reporting question: What specific cost categories are driving the widening gap? Is it pharmaceuticals, administrative overhead, or specialist fees?

Finding 2: Turkey's spending jumped 34% between 2021 and 2023 From $1,650 to $2,210 — the fastest growth rate in the dataset by a wide margin. The OECD average grew 8% over the same period. This is not a statistical artefact; the increase is consistent across both years. Reporting question: Is this driven by post-earthquake health infrastructure spending, a policy expansion, or currency/PPP adjustment effects? Check whether the increase appears in nominal local-currency terms or only in PPP-adjusted dollars.

Finding 3: Japan's spending barely moved (+2%) despite an aging population Japan spent $4,691 in 2023, up from $4,587 in 2018. In a country where 29% of the population is over 65, flat healthcare spending is the outlier — it contradicts the standard assumption that aging populations drive cost increases. Reporting question: Is Japan suppressing costs through price controls, care rationing, or efficiency gains? Or is the flat figure masking a shift from public to out-of-pocket spending?

Finding 4: Luxembourg appears as a statistical outlier at $7,890, but it's probably noise Luxembourg's small population (660,000) and high cross-border workforce distort per-capita calculations. Many people receiving care in Luxembourg aren't counted in the population denominator. This is a known measurement issue in OECD data. Reporting question: None — flag this as a data limitation rather than a story.

Summary: 4 outliers identified in 180 data points (30 countries × 6 years). Finding 1 is well-trodden but the acceleration angle is fresh. Finding 2 is the strongest lead for original reporting — under-covered and verifiable. Finding 3 is a strong counterintuitive angle for a feature. Finding 4 is noise. Additional data that would help: spending broken down by cost category (hospitals, pharma, admin) and out-of-pocket vs. public expenditure splits.

Known limitations

• Cannot run statistical calculations on raw data. Works from summary statistics, described datasets, or pasted tables. For large datasets, provide aggregated summaries rather than row-level data.
• Outlier detection is based on described distributions and contextual knowledge, not automated statistical tests. Results should be verified with proper statistical tools before publication.
• Cannot access live databases or APIs. Works with the data you provide in the conversation.
• Context-dependent anomalies (e.g., knowing that Turkey had an earthquake in 2023) rely on the assistant's training data. Very recent events may not be reflected.

Related skills

• data-story-finder
• statistics-explainer
• chart-type-advisor
• methodology-explainer