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Foster and Rahmstorf (2026) remove El Niño, volcanic and solar influences from five global temperature records with a regression fitted to those records. They then report high confidence that warming accelerated, using critical values that treat the adjusted series as raw data. We ran their whole procedure, adjustment and tests together, on 2000 records per dataset, simulated under their own null: a straight-line trend since 1970 plus stationary noise. With noise fitted under that null, their changepoint test at nominal level 0.05 rejected 0.059 to 0.07 of null records, and the observed statistics had full-pipeline p of 0.0005 to 0.0025 in every record. Their quadratic test fared worse, with full-pipeline p up to 0.054. Under long-memory noise the changepoint evidence weakened, to p of at most 0.04, but held. The acceleration finding rests on the changepoint test.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: A numpy port of Foster and Rahmstorf's 2026 adjustment for El Niño, volcanic and solar influences reproduces their published adjusted NASA, NOAA, HadCRU, Berkeley and ERA5 series to within 0.0001 °C, and the changepoint test of Beaulieu et al. (2024) on the adjusted annual means for 1970-2024 gives maximum slope-change statistics from 4.91 to 6.68 with the change in 2013 or 2014 and nominal AR(1) Monte Carlo p-values from 0.00014 to 0.0033, against 0.041 to 0.19 for the unadjusted series.
• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: When 2,000 records per dataset are simulated under Foster and Rahmstorf's null hypothesis (a linear trend since 1970, their fitted exogenous terms, and ARMA(1,1) noise fitted under the null) and passed through their whole adjustment and changepoint test, the nominal 5% test rejects 5.9% to 7.0% of null records, and the observed statistics have full-pipeline p-values from 0.0005 to 0.0025 in all five records, so the changepoint finding is robust by the pre-registered rule.
• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: Under the same full-pipeline null, Foster and Rahmstorf's quadratic test with the ARMA(1,1) correction of Foster and Rahmstorf (2011) rejects 5.8% to 8.0% of null records at nominal 5% on monthly values and 7.2% to 9.2% on annual means, and the observed quadratic terms have full-pipeline p-values from 0.0015 to 0.054 (monthly) and 0.0005 to 0.032 (annual), below 0.02 in only three records for monthly values and two for annual means, so the reported confidence above 98% in all five records does not hold and the finding is mixed by the pre-registered rule.

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How sensitive is evidence for recent global warming acceleration to the final years included and the model of natural variability? A registered audit uses a frozen NASA global annual temperature series. A fixed-breakpoint model estimates a warming-rate increase of 0.232 °C per decade, with a confidence interval from 0.106 to 0.358. A search over possible breakpoints gives p = 0.0271 with fitted autocorrelation, but p = 0.4078 when the recent endpoint years are excluded. Stronger assumed autocorrelation also weakens the test. These are conditional statistical results about an unadjusted series, with limited simulated power for smaller slope changes.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: In the frozen NASA GISTEMP annual series for 1970-2025, a fixed-2015-knot model estimates a warming-rate increase of 0.232 degrees Celsius per decade with a 95% HAC interval of 0.106 to 0.358, while the registered breakpoint-search p-value is 0.0271 through 2025, 0.4078 through 2022, and 0.1726 through 2025 when the AR(1) lag coefficient is fixed at 0.6.

How much false-positive error can arise when a nominally exact test treats clustered subsamples as independent? An exact finite benchmark enumerates null distributions for 75 balanced designs. In the selected beta-binomial design, the cell-level Fisher test rejects with probability 0.21567065919813758, compared with 0.0436374106894531 for a test using the known clustered null. With a single observation per donor, the Fisher rejection probability is 0.021484375. These are exact probabilities conditional on the specified toy model. They demonstrate calibration failure from ignoring dependence, without evaluating real biological pipelines or ranking practical alternatives.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: For the specified null of five independent donors per arm and twenty conditionally independent Bernoulli observations per donor with latent Beta(5,5) success probabilities (within-donor correlation 1/11), the cell-level two-sided probability-ordering Fisher test at nominal alpha=0.05 rejects with exact-model probability 0.215670659198, versus 0.043637410689 for the known-clustered-null absolute-difference-tail oracle. The finite 75-design grid has 31 cell-level Fisher probabilities above nominal, while all 75 oracle probabilities and all 15 independent-observation controls are at or below nominal. These are conditional toy-model calibration results, not evaluations of real biological analysis methods.

How does repeated monitoring change false-positive rates for a coin-toss test? This resource computes exact rejection probabilities and expected observation counts using integer path weights. At the selected horizon, a fixed binomial test rejects a fair-coin null with probability 0.04431304005703379, repeated binomial testing with probability 0.20205809786793333, and a likelihood-ratio test with probability 0.04088964341296718. The benchmark provides 36 scenario rows and passes 18 exhaustive-enumeration comparisons. It supplies an auditable numerical illustration of established optional-stopping behavior, without asserting a new statistical theorem.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: A dependency-free exact-path benchmark computes rejection probabilities and expected sample counts for fixed, repeatedly monitored, and likelihood-ratio tests across the declared Bernoulli scenarios, and agrees with exhaustive enumeration in every declared oracle case.

Can unknown overlap between adverse-event terms change the direction of published composite-event estimates? We reconstructed the frozen registry comparisons and calculated every possible range of distinct affected participants consistent with the selected term counts. For adult serious suicidal behaviour, the resulting Mantel–Haenszel risk-ratio bounds are 1.712 to 2.1962; for adult serious suicidality, 1.2154 to 1.8476. The corresponding pediatric bounds are 1.0045 to 1.4773 and 1.8144 to 2.8091. All remain above the null. Unknown overlap therefore changes the magnitude but cannot reverse these fixed-data point estimates under the source's cohort and outcome definitions. These are identification bounds, not confidence intervals or estimates of causal treatment effects.

• Published, Reproduced.Published, Reproduced: In the frozen antidepressant registry dataset, conditional on source cohort selection, denominators, term coding, and absent-term imputation, sharp Mantel-Haenszel risk-ratio bounds relative to selected within-group event-count marginals are 1.7120261533 to 2.1961879882 for adult serious suicidal behaviour, 1.2154409158 to 1.8475901940 for adult serious suicidality, 1.0045047792 to 1.4773331886 for pediatric serious behaviour, and 1.8144436696 to 2.8090979871 for pediatric serious suicidality. Thus unknown participant overlap alone cannot reverse the direction of these fixed-data point estimates; these are not confidence intervals or causal treatment effects.