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    • A
      license
      A
      quality
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      maintenance
      Eval-integrity statistics for AI benchmark claims — multiple-testing correction, power/MDE for model gaps, judge-bias and leaderboard-rank checks. Catches a benchmark number that won't survive a second look.
      9
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Checks whether a trading backtest survives its own statistics: deflated Sharpe, multiple-testing correction against a best-of-N-noise benchmark, minimum track record length, and fill realism. Takes no market data and no API keys, and cannot recommend a trade — it only reports that a result is weaker than claimed or not yet provable.
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Provides verified statistical inference and hypothesis testing tools, including t-tests, effect sizes, power analysis, and multiple comparisons correction, with assumption checks and citations.
      37
      130 PyPI
      1
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Answers "sales dropped since last week — where?" by comparing a target period against a weekday-adjusted baseline and localizing which attribute combinations (e.g. channel=web, or Tuesday nights) explain the shift. Runs fully offline on your own CSV — no API key, no ML training, read-only.
      2
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Glass-box statistical analysis for time series and business data: 19 research-grade methods, cited findings, and measured detector false-fire rates shipped as a calibration corpus. Agents cite real math instead of inventing it.
      7
      47 PyPI
      5
      Apache 2.0

    TDQS

    A3.8/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool targets a distinct statistical question: A/B testing, change detection, forecasting, denominator shift, forecast evaluation, and multiple testing correction. No overlap in purposes.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in lowercase snake_case, e.g., did_it_change, forecast_next, which_metrics_matter. No deviations.

    Tool Count5/5

    6 tools is an ideal number for a focused statistics toolkit, covering essential operations without being overwhelming or sparse.

    Completeness4/5

    The set covers key statistical tasks (A/B testing, change detection, forecasting, multiple testing), but lacks a sample size/power analysis tool, which is a minor gap.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues