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B=1N∑i=1N(pi−oi)2B = \frac{1}{N}\sum_{i=1}^{N} (p_i - o_i)^2

Why this formula appears here

The Brier score is the mean squared error between a stated probability and the binary outcome (1 if it happened, 0 if it did not): B=1N∑i=1N(pi−oi)2B = \frac{1}{N}\sum_{i=1}^{N} (p_i - o_i)^2. where pip_i is the forecaster’s stated probability on question i and oio_i ∈\in \{0,1\} is the realized outcome. Lower is better; a perfectly calibrated coin-flip forecaster scores 0.25 on a 50/50 question, and a forecaster who says 100 percent and is wrong scores the maximum possible 1.0.

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NN

Symbol N

N occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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ii

Symbol i

i appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

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pip_i

Symbol p_i

the forecaster’s stated probability on question i and oio_i ∈\in \{0,1\} is the realized outcome.

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i=1i=1

Starting index or lower bound: i=1

This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.

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NN

Ending index or upper bound: N

This label says where the repeated addition, multiplication, or accumulation stops. It sets the last term or end of the range.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.

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Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

B=1N∑i=1N(pi−oi)2B = \frac{1}{N}\sum_{i=1}^{N} (p_i - o_i)^2

Equation 1 · Future Studies

Futures Methods and Technological Scenarios in Practice: An Advanced Technical Guide

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

The Brier score is the mean squared error between a stated probability and the binary outcome (1 if it happened, 0 if it did not): B=1N∑i=1N(pi−oi)2B = \frac{1}{N}\sum_{i=1}^{N} (p_i - o_i)^2. where pip_i is the forecaster’s stated probability on question i and oio_i ∈\in \{0,1\} is the realized outcome. Lower is better; a perfectly calibrated coin-flip forecaster scores 0.25 on a 50/50 question, and a forecaster who says 100 percent and is wrong scores the maximum possible 1.0.

Meanings in this article

  • pip_i: the forecaster’s stated probability on question i and oio_i ∈\in \{0,1\} is the realized outcome.
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