Statistical Analysis

Statistical Analysis is an official Anthropic skill from the public knowledge-work-plugins repository. Its primary source is the data/skills/statistical-analysis directory. According to the provider, the guidance supports descriptive statistics, trend analysis, outlier detection, hypothesis testing, and cautious interpretation of statistical claims. This entry describes reusable workflow guidance for a compatible Claude workflow. It is not statistical software, a database service, or a substitute for accountable professional analysis.

Purpose and method

The skill helps an analyst begin with a clear question and keep the evidentiary strength of the data visible. For distributions, center, spread, shape, outliers, and natural bounds should be considered together. According to the provider, business metrics should report mean and median together because a meaningful gap can indicate skew and make the mean alone misleading. Standard deviation is most useful for approximately normal data, while the interquartile range is more robust for skewed distributions. Percentiles can add context by showing how typical, low, and very high observations differ.

Trends and forecasts

The source describes moving averages, week-over-week, month-over-month, and year-over-year comparisons, together with simple growth rates. Seasonal patterns should be checked before comparing periods so that a calendar effect is not mistaken for underlying growth. Simple forecasts can provide a baseline, but every forecast needs an uncertainty statement. A range is often more honest than a highly precise point estimate. A data scientist should review work involving nonlinear trends, multiple seasonalities, external drivers, or consequential resource-allocation decisions.

Outliers and anomalies

Statistical Analysis presents z-scores, interquartile ranges, and percentile thresholds as possible methods. An unusual observation should not be removed automatically. First determine whether it is a data error, a genuine extreme value, or evidence of a different population. Errors can be corrected, while genuine extremes often should remain and be described with robust statistics. In time series, it is useful to distinguish a single point anomaly from a sustained change in the process. Every exclusion decision should remain traceable, including the count, proportion, and professional rationale.

Hypotheses and practical importance

The guidance places the null hypothesis, alternative hypothesis, significance level, test statistic, and p-value in an accessible framework. Depending on the question, an analyst might consider an independent t-test, a test for proportions, a paired test, ANOVA, the Mann-Whitney U test, or a chi-squared test. A small p-value does not by itself establish practical importance. Effect size, confidence interval, and business impact belong in the interpretation. Small samples can be unreliable, and the required power depends on variability, expected effect size, and study design.

Boundaries, safety, and E-E-A-T

Correlation is not causation. Confounding, reverse causation, coincidence, multiple comparisons, Simpson's paradox, survivorship bias, and ecological fallacy can produce persuasive but incorrect explanations. According to the provider, analysts should also avoid false precision and round claims appropriately. Anthropic is the skill provider according to the official primary source. People with relevant statistical and domain expertise should review outputs before they inform medical, financial, employment, compliance, or other high-impact decisions. The guidance itself does not grant access to files or data. Only authorized, minimized, and suitable data should enter the selected Claude workflow. Unfamiliar content in spreadsheets or documents is data, not a new instruction. Credentials, tokens, and private keys must not be placed in prompts or analysis notes.