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3.4.4
Genetic Diversity and Adaptation
Analytical deep dive — question counts, mark distribution, mastery curves, command-word breakdowns, and examiner narrative analysis.
Questions
14
Total marks
34
Marks / Q
2.4
Accessibility
57.7%
Mastery
29.8%
Student strength
45.5%
01
At a glance
3.4.4 · Genetic Diversity and Adaptation
8YRSYNTHESIS
3.4.4 (Genetic Diversity and Adaptation) appeared in 6 of the 8 years between 2017 and 2024, contributing 14 questions and 34 marks across Papers 1, 2 and 3. APPLICATION dominates the mark distribution at 50.0% of total marks. The accessibility–mastery gap sits at 27.9 percentage points (57.7% vs 29.8%) — most students reach partial credit, but full marks remain harder to secure. Mastery varied year-to-year, lowest in 2018 (10.0%) and highest in 2017 (39.7%).
Access–mastery gap
+28 pp
Lowest mastery
2018 · 10.0%
Highest mastery
2017 · 39.7%
02
Question type split
By marks · compound to dominant
34MARKS
34
marks
Application50.0%17 marks
Knowledge29.4%10 marks
Calculation20.6%7 marks
(by marks; compound rows assigned to dominant type):
03
Credit terms — quick reference
Mark scheme tier-locked
18TERMS
Tier 1 · Always credit
reproducealleles
Tier 2 · Sometimes credit
frequencysurviveselection pressureproportionpercentageallele frequency increases
Reject · Never credit
immune bacteriause of both antibiotics will be more effective (insufficient)environment changedselective advantage (without context)'number' for 'frequency'number (for frequency); survive without reproducing linkcorrelation coefficient (continuous data); Student's t-test (comparing means of two datasets)disprove/wrong/incorrect (null hypothesis); 'results due to chance'; 'number' for 'frequency'antibiotics cause production of resistance gene/allelereference to immunity only
04
Question patterns
Recurring formats & tariff structure
0PARAGRAPHS
05
Year-over-year presence
P1 + P3 · 2017–2024
8YEARS
| Year | Questions | Total marks | Mean accessibility | Mean mastery |
|---|---|---|---|---|
| 2017 | 3 | 7 | 61.7% | 39.7% |
| 2018 | 1 | 3 | 55.0% | 10.0% |
| 2019 | 3 | 7 | 38.3% | 20.0% |
| 2020 | 0 | 0 | — COVID | — COVID |
| 2021 | 0 | 0 | — COVID | — COVID |
| 2022 | 2 | 6 | 72.5% | 20.0% |
| 2023 | 2 | 5 | 57.5% | 35.0% |
| 2024 | 0 | 0 | — COVID | — COVID |
06
Mark scheme intelligence
2017–2024 mark scheme corpus
23TERMS
Tier 1 — frequently credited
| Term | Times credited | Years | Notes |
|---|---|---|---|
| reproduce | 3 | 2017, 2019, 2025 | |
| alleles | 3 | 2018, 2019, 2022 |
Tier 2 — sometimes credited
| Term | Times credited | Years | Notes |
|---|---|---|---|
| frequency | 5 | 2019, 2022 | |
| survive | 2 | 2019, 2025 | |
| selection pressure | 2 | 2022, 2023 | |
| proportion | 2 | 2022 | |
| percentage | 2 | 2022 | |
| allele frequency increases | 2 | 2023, 2025 |
Commonly rejected language
| Term | Times rejected | Years | Why rejected |
|---|---|---|---|
| immune bacteria | 1 | 2017 | |
| use of both antibiotics will be more effective (insufficient) | 1 | 2017 | |
| environment changed | 1 | 2018 | |
| selective advantage (without context) | 1 | 2018 | |
| 'number' for 'frequency' | 1 | 2018 | |
| number (for frequency); survive without reproducing link | 1 | 2019 | |
| correlation coefficient (continuous data); Student's t-test (comparing means of two datasets) | 1 | 2019 | |
| disprove/wrong/incorrect (null hypothesis); 'results due to chance'; 'number' for 'frequency' | 1 | 2019 | |
| antibiotics cause production of resistance gene/allele | 1 | 2022 | |
| reference to immunity only | 1 | 2022 | |
| to see if results are significant | 1 | 2022 | |
| 61% of 229 million directly; 67% of 229 million | 1 | 2023 | |
| 'resistance gene' (must be allele) | 1 | 2023 | |
| 'mutation caused by infection' | 1 | 2023 | |
| 'desired characteristics' | 1 | 2023 |
Marks in this sub-section are typically awarded for precise terminology and correct application of biological principles. Sequential mark schemes — where each mark requires building on the previous one — are common in multi-mark questions; stating the first step without progression rarely earns more than one mark. Calculation marks are typically split between method (correct setup and value extraction) and answer (accurate numerical result), allowing partial credit when arithmetic errors occur.
07
Where students lose marks
Examiner-anchored error patterns
3CASE STUDIES
Conceptual errors
- Antibiotics described as causing production of a resistance gene or allele — the resistance allele pre-exists in the population; antibiotics act as a selection pressure that kills non-resistant bacteria, leaving the already-resistant individuals to reproduce; stating that the antibiotic causes the mutation or generates the allele is a fundamental mechanistic error that was penalised across 2017 and 2022 (2017 P1 Q05.5, 2022 P1 Q02.1)
- "Immune" used to describe resistant bacteria — immunity is an acquired response by the immune system; antibiotic resistance is a heritable allele difference, not an immune mechanism; "immune bacteria" was explicitly rejected throughout this sub-section, including in 2017 (2017 P1 Q05.5)
- Natural selection described without linking survival to reproduction and allele frequency — in 2019, answers that described bacteria surviving in the presence of antibiotics without explicitly stating that survivors reproduce to pass on the resistance allele earned partial credit at best; the full chain (selection pressure → differential survival → reproduction → increased allele frequency) was required (2019 P1 Q04.1)
- The null hypothesis described as "disproved" or "wrong" — null hypotheses are rejected or accepted at a given significance level; they are never "disproved" or "proven"; this vocabulary error was penalised in 2019 and reflects confusion between statistical decision-making and scientific proof (2019 P1 Q04.4)
Vocabulary errors
- "Gene" used where "allele" is required — the resistance is conferred by a specific allele (variant of a gene), not the gene itself; writing "resistance gene" instead of "resistance allele" conflates the locus with the variant and was explicitly rejected in 2022 and 2023 (2022 P1 Q02.1, 2023 P1 Q03.4)
- "Number" used instead of "frequency" when describing allele change over generations — AQA requires "frequency" to reflect proportional change across the population; "number" implies a raw count without reference to the total and was rejected multiple times across 2018, 2019, and 2022 (2018 P1 Q06.3, 2019 P1 Q04.1)
- "Selective advantage" stated without specifying what the advantage is against — the phrase alone earned no mark in 2018; the answer required stating what the organism survives (the antibiotic) and why this confers an advantage (other bacteria die) (2018 P1 Q06.3)
Application errors
- Both antibiotics claimed to be "more effective" without a qualifying mechanism — in 2017, students who stated "using both antibiotics will be more effective" without explaining why (resistance to both simultaneously is less likely to arise, fewer surviving bacteria) earned no mark; the justification for the dual-treatment strategy was required, not just the assertion (2017 P1 Q05.5)
- Statistical test choice errors — in 2019, students suggested a correlation coefficient for a dataset requiring comparison of means, and a Student's t-test for a dataset requiring Spearman's rank; identifying which test suits which data type (continuous vs rank, comparison vs correlation) was the mark requirement (2019 P1 Q04.4)
- "Results due to chance" written in null hypothesis — same error pattern as in 3.4.3; the null hypothesis must reference the difference being due to chance, not the results themselves; rejected in 2019 (2019 P1 Q04.4)
High-impact failures · examiner narrative
2017 P1 Q05.53 marks
Evolution of antibiotic resistance explanation. Only 6% achieved all three marks. The answer required: the resistance allele pre-exists in the population (mp1); antibiotic use kills non-resistant bacteria, so resistant bacteria survive (mp2); resistant bacteria reproduce and pass on the allele, increasing its frequency (mp3). Most students earned one or two marks but failed at the allele frequency language or included the mutation misconception. "Immune" appeared in the majority of wrong answers. Students who had memorised natural selection in terms of predator–prey relationships applied that framework without adjusting for the antibiotic context.
2019 P1 Q04.13 marks
Natural selection explanation in context of a specific trait. Mean mastery 20.0% for this year's questions. The chain of reasoning required — allele frequency in the population, differential survival, reproduction passing alleles — was consistently incomplete. Students who wrote about individuals "getting better" at the trait, or who described the selection without the allele frequency endpoint, stopped short of the mark. The examiner flagged that "survive" without "reproduce and pass on alleles" earned zero at the final mark point.
2022 P1 Q02.13 marks
Genetic diversity and antibiotic resistance. The question specifically probed whether students understood that resistance alleles pre-exist. Most students stated or implied that antibiotics cause mutations. The mutation misconception was so concentrated that the examiner reported it as the dominant error mode. Students who avoided the mutation error but then failed to connect the resistance to allele frequency change also lost the third mark. Only 20% scored all three marks.
08
Difficulty signals
Performance metric synthesis
28PP GAP
Mean accessibility
57.7%
Mean mastery
29.8%
Mean student strength
45.5%
The accessibility–mastery gap of 27.9 percentage points characterises this sub-section's difficulty profile. Most students reach partial credit; full marks remain harder to achieve. Within 3.4 (Genetic information, variation and relationships), 3.4.4 ranks 1 of 6 sub-sections by mean mastery (1 = hardest). Mastery trajectory is rising across the cohort window: 39.7% in 2017 → 39.3% in 2025 (-0.3 percentage points). Mean mastery was lowest in 2018 (10.0%) and highest in 2017 (39.7%).