Sample Size Robustness Check

Overview

This pillar acts as a statistical watchdog, evaluating the sample size behind historical patterns. It flags analyses that rely on data too sparse to be reliable, helping you avoid decisions based on statistical flukes.

What It Does

It analyzes the number of historical data points (the 'N') used to establish a pattern or correlation. The pillar compares this sample size against established statistical thresholds to determine if the pattern is robust or likely due to random chance. It calculates confidence intervals and flags instances where the range of potential outcomes is too wide to be predictive.

Why It Matters

Many predictions rely on 'this happened X out of Y times before'. This pillar provides a crucial reality check, preventing traders from over-investing in patterns that lack statistical significance. It separates genuine historical edges from random noise, protecting your capital from weak assumptions.

How It Works

First, the pillar identifies the number of occurrences (N) in a historical dataset supporting a specific prediction. Next, it calculates the confidence interval around the observed probability, showing the true range of possibilities. Finally, it compares the sample size and interval width against predefined thresholds to issue a 'robust' or 'low-sample' warning.

Methodology

The pillar uses a Wilson score interval for calculating confidence, which is accurate for small sample sizes. It flags any analysis where the sample size N is below 30 as a primary warning. A secondary flag is triggered if the 95% confidence interval for an outcome's probability is wide enough to cross a critical decision threshold, like 50%.

Edge & Advantage

It provides a defensive edge by systematically filtering out statistically weak signals that often trap traders. This prevents costly mistakes based on anecdotal evidence or seemingly convincing but under-sampled patterns.

Key Indicators

• N-Count Threshold

  high

  Flags if the number of historical data points (N) is below a statistically significant minimum, typically 30.

• Confidence Interval Width

  high

  Measures the range of uncertainty around an observed probability. A wide interval indicates low confidence.

• P-Value Significance

  medium

  Assesses the probability that the observed pattern occurred by random chance. A high p-value suggests the pattern is not significant.

Data Sources

• Primary Pillar Data

  Uses the historical dataset from the primary analysis pillar being evaluated.

• Market Resolution Data

  Historical outcomes from similar prediction markets to establish a baseline.

Example Questions This Pillar Answers

• → Is the 'January Barometer' a reliable indicator for the stock market this year?
• → Will a specific team win, given they have won their last 3 matches against this opponent?
• → Is a political candidate's recent polling surge significant or just statistical noise?

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