How this calculator was checked

Written for people who want to audit the claim rather than take it on trust: tutors, schools, and anyone who has been told before that a calculator is “accurate” without being told against what.

The short version

We ran all 5,103 children who sat the 2026 CSSE test through this calculator, using the marks CSSE itself publishes, and compared every result against CSSE’s own published standardised scores. The largest disagreement anywhere in the distribution — at the minimum, the maximum, or any quantile between — is 0.54 of a point. Across the middle 90% it is 0.25.

The check lives in the repository as scripts/verify-csse.ts and is 21 of 21. It was last run against the live published files on 21 August 2026.

Why CSSE can be calculated exactly and nothing else can

Standardising a raw mark requires the cohort mean and standard deviation for that paper, in that year. Those are the numbers exam boards treat as confidential. CSSE is the exception: its annual Standardisation Report states the formula, the per-paper constants and the age adjustment outright.

So for a past year we are not modelling CSSE’s calculation. We are running it. That is also why this page can exist at all — CSSE publishes enough for the working to be shown.

The formula

Each paper is standardised on its own:

standardised = ((mark − cohort mean) ÷ standard deviation) × 15 + 100

A per-day age adjustment is added to each paper, based on how many days younger the child is than the oldest in the year group. The two standardised, age-adjusted papers are added together, and the total is multiplied by 1.5.

That multiplier is the single most common error in 11+ calculators. Copy the formula out of CSSE’s PDF and “×1.5” can come through as “15”. Drop it and every child appears to fail — including a child with full marks on both papers. We hold tests that fail the build if our own output ever loses it.

A useful sanity check, and one CSSE states itself: a child born on 1 September scoring exactly the cohort average on both papers lands on 300.

How we verified it

CSSE publishes two files per year under Freedom of Information: every candidate’s raw marks, and every candidate’s standardised total. That makes an end-to-end check possible. We compute the whole cohort from the raw file and compare our distribution against the published one.

CheckResult
Cohort constantsour transcription vs the same statistics recomputed from the candidatesagree to 0.006
Distributionworst disagreement at any quantile, including both extremes0.54
Distribution, 5th to 95th percentilewhere almost every real child sits0.25
Candidates checkedeveryone who sat, not a sample5,103

The thirteen children who were not there

Worth setting out, because it is the reason the numbers above are as close as they are, and because it is the kind of thing that quietly breaks a calculator nobody checks.

When we first compared our constants with CSSE’s, four figures were out by as much as 0.19. Small, but not small enough to be rounding, and we would not publish until we understood why.

CSSE’s published raw score file for 2026 entry contains 5,116 rows. 13 of them scored zero on both papers — children who did not sit the test. CSSE publishes them in the raw file and, quite correctly, leaves them out of the standardisation. Against the 5,103 who did sit, every constant agreed to within 0.006, and the worst disagreement across the whole distribution fell from more than eleven points to 0.54.

We keep the check that found this and rerun it whenever the figures change. What we no longer claim is that the rule is general, because it turned out not to be. For 2020 entry CSSE’s own published cohort of 5,465 includes its one zero-zero candidate rather than excluding it. CSSE has not been consistent about this, so the honest procedure is to reconcile every year against CSSE’s published figures and say which pool was used — not to carry forward a rule drawn from a single year.

What a mark was worth

Everything above is about whether this calculator computes the right number. This is about why the number is worth computing at all — and it is the part most people are surprised by.

35 marks in English. In 2026 entry that standardised to 100.03, almost exactly the county average. In 2020 entry the same 35 marks was 114.23.

That is 14.2 standardised points on one paper. CSSE adds the two papers together and multiplies by 1.5, so it reaches the score that decides a place as 21.3 points — against a qualifying score of 303. Both years are ones CSSE published a Standardisation Report for, so both figures are CSSE’s own. Neither is our estimate.

It is tempting to call 2020 “a harder paper”. We won’t, because it cannot be shown. A cohort average moves with the difficulty of the paper and with who sat it — the entry ranged from 5,635 candidates in 2019 down to 4,807 in 2022 — and nothing CSSE publishes separates the two. What can be shown is the part that matters when you are holding a marked paper: the same mark was worth different scores in different years. That is true whatever moved the average.

2019 entry settles it. It has the highest English average of the eight years — 37.64 — and the lowest Maths average of the eight — 24.78. The same children, the same morning, the two papers moving in opposite directions. So a year that was generous on one paper was not generous on both, and a raw mark cannot be read without that year’s figures for that paper. Across all eight years the English average travelled 10.4 marks and the Maths average 6.9.

English Mathsaverage mark
0102030402019 entry, English: average 37.642019 entry, Maths: average 24.7820192020 entry, English: average 27.272020 entry, Maths: average 28.1220202021 entry, English: average 32.392021 entry, Maths: average 28.7820212022 entry, English: average 31.422022 entry, Maths: average 31.6820222023 entry, English: average 35.152023 entry, Maths: average 30.3520232024 entry, English: average 31.462024 entry, Maths: average 28.3420242025 entry, English: average 34.362025 entry, Maths: average 27.5120252026 entry, English: average 34.992026 entry, Maths: average 27.302026
The average mark on each CSSE paper, by entry year. Bars rather than a line because these are eight separate cohorts, not a trend — and next year’s figures cannot be known until that year has sat the paper.
CSSE cohort figures by entry year, with the source of each year’s mean and standard deviation.
EntryEnglishMathsSat
2019computed by us37.64best mark 5924.78best mark 595,635no absentees
2020CSSE’s figure27.27best mark 5228.12best mark 605,4641 did not sit
2021computed by us32.39best mark 5728.78best mark 604,9881 did not sit
2022CSSE’s figure31.42best mark 5631.68best mark 604,807no absentees
2023computed by us35.15best mark 5830.35best mark 604,972no absentees
2024CSSE’s figure31.46best mark 5428.34best mark 604,974no absentees
2025computed by us34.36best mark 5627.51best mark 605,287no absentees
2026CSSE’s figure34.99best mark 5627.30best mark 585,10313 did not sit

Four of those eight years carry a CSSE Standardisation Report, and for those the mean and standard deviation are CSSE’s own constants — not our estimate of the cohort but the actual divisor CSSE divided by. For the other four CSSE published every candidate’s marks but no report, so those figures are computed by us from that raw data. The table says which is which on every row, and the worked example above deliberately uses only the first kind.

Two smaller things the table will raise for anyone reading closely. The “best mark” figures are the highest any candidate is recorded as scoring, not paper totals — CSSE has never published what either paper is out of. And where a year had absentees, the computed figures exclude them; for the four years we computed, three had none at all and the fourth had one, which moves its average by about a hundredth of a mark.

None of this lets anyone predict next year. The cohort statistics do not exist until the cohort has sat the paper, which is the honest reason no tool can convert a mock mark into next year’s standardised score.

What we still cannot know

Stating this plainly matters more than the rest of the page. A tool that sounds certain about everything is telling you something about its authors rather than about the test.

Sources

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