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AKT Evidence & Data Interpretation: The 2026 Playbook

A ruthless method for AKT charts, confidence intervals, risk tools and EBM traps—so you stop losing easy marks on “data questions”.

Review date
Feb 2026
Reading time
12 min read
Author
Dr. Kola Tytler MBBS CertHE MBA MSt MRCGP
On this page

Key points

  • AKT EBM/data questions are often <strong>method questions</strong>, not medicine questions: your job is to interpret what’s shown (denominator, axis, rate vs count).
  • Use a <strong>repeatable 6-step scan</strong> for every chart/table before you even look at the answer options.
  • Most lost marks come from: <strong>rates vs totals</strong>, <strong>relative vs absolute risk</strong>, and misreading <strong>confidence intervals</strong>.
In the AKT, evidence-based practice questions are designed to test your interpretation of data presentations (graphs, tables, audit-style summaries) and your ability to reason about risk and uncertainty. The fastest improvement comes from using a fixed method—like a pilot checklist—rather than “hoping the graph makes sense”.

The mental model

Treat every EBM/data question as: <strong>What is being measured?</strong> → <strong>In whom?</strong> → <strong>Over what time?</strong> → <strong>Compared to what?</strong> → <strong>How certain is the estimate?</strong>
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Step 1 — Identify the unit of meaning (10 seconds)

Before reading options, state the unit out loud: <strong>rate per 1,000</strong>, <strong>percentage</strong>, <strong>incidence</strong>, <strong>odds ratio</strong>, <strong>hazard ratio</strong>, <strong>absolute number</strong>. Many “trick” questions are just denominator traps.
2

Step 2 — Read the axes like a lawyer (15 seconds)

Confirm: axis labels, time window, population, whether the plot is per 1,000 / per 100,000, whether it is <strong>log scale</strong>, whether there’s a second axis, and whether the plotted values are <strong>adjusted</strong> or raw.
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Step 3 — Convert the figure into one sentence (10 seconds)

Write (mentally): “In population X, outcome Y is higher/lower in group A vs B over time T.” If you cannot summarise in one sentence, you are not ready to answer.
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Step 4 — Separate association from causation (10 seconds)

If it’s observational: default to <strong>association</strong>, consider confounding and bias. If it’s RCT/meta-analysis: consider randomisation, effect size, and certainty.
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Step 5 — Use the CI rule properly (10 seconds)

For ratios (RR/OR/HR): CI crossing <strong>1</strong> → not statistically significant at that level. For differences (mean difference): CI crossing <strong>0</strong> → not statistically significant. Then ask: even if significant, is it clinically meaningful?
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Step 6 — Translate relative risk into absolute impact (30 seconds)

If you can estimate baseline risk, convert to <strong>absolute risk reduction</strong> and then <strong>NNT</strong> (NNT ≈ 1 / ARR). The AKT likes testing whether you can spot when a “big relative risk” is actually small absolute benefit.

The AKT Data Question “Trap List” (memorise this)

  • Rate vs count: “per 1,000” is not the same as “number of events”.
  • Proportion vs number: a higher proportion can occur with fewer absolute events (different denominators).
  • Correlation ≠ causation: do not infer interventions caused outcomes from cross-sectional audit-style charts.
  • CI ≠ range of patient outcomes: it’s uncertainty around the estimate, not variability in individuals.
  • p-value ≠ clinical relevance: significance does not mean “worth doing”.
  • Screening metrics: sensitivity/specificity are test properties; PPV/NPV depend on prevalence.

What to do when you feel unsure

Do not “guess from vibes”. Apply the method: unit → axes → one-sentence summary → comparison → uncertainty. Your score improves because your process is stable, not because you learn more facts.
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The 20-minute weekly drill (high ROI)

Once weekly, do 10 AKT-style data questions timed. For each wrong answer, write a <strong>one-line error label</strong> (e.g., “denominator trap”, “CI crossing 1”, “association/cause confusion”). After 4 weeks, your pattern will be obvious—and fixable.
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Build a micro-cheat-sheet (one page only)

Your one page should contain: CI rule, RR→ARR→NNT conversion, sensitivity vs PPV, and 5 most common graph traps. If it becomes 3 pages, you have lost the point.
SourceRCGP: AKT Example Questions (with answers) — Nov 2025 (official PDF)
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SourceRCGP: Evidence & Data Interpretation in the AKT (official PDF)
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SourceRCGP: Preparing for the AKT (official page)
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  • Before reading options, state the unit out loud: rate per 1,000, percentage, incidence, odds ratio, hazard ratio, absolute number. Many “trick” questions are just denominator traps.

    Return to this section

  • Confirm: axis labels, time window, population, whether the plot is per 1,000 / per 100,000, whether it is log scale, whether there’s a second axis, and whether the plotted values are adjusted or raw.

    Return to this section

  • Write (mentally): “In population X, outcome Y is higher/lower in group A vs B over time T.” If you cannot summarise in one sentence, you are not ready to answer.

    Return to this section

  • If it’s observational: default to association, consider confounding and bias. If it’s RCT/meta-analysis: consider randomisation, effect size, and certainty.

    Return to this section

  • For ratios (RR/OR/HR): CI crossing 1 → not statistically significant at that level. For differences (mean difference): CI crossing 0 → not statistically significant. Then ask: even if significant, is it clinically meaningful?

    Return to this section

  • If you can estimate baseline risk, convert to absolute risk reduction and then NNT (NNT ≈ 1 / ARR). The AKT likes testing whether you can spot when a “big relative risk” is actually small absolute benefit.

    Return to this section
  • Return to this section
  • Return to this section
  • Return to this section
  • Return to this section
  • Return to this section
  • Return to this section

  • Once weekly, do 10 AKT-style data questions timed. For each wrong answer, write a one-line error label (e.g., “denominator trap”, “CI crossing 1”, “association/cause confusion”). After 4 weeks, your pattern will be obvious—and fixable.

    Return to this section

  • Your one page should contain: CI rule, RR→ARR→NNT conversion, sensitivity vs PPV, and 5 most common graph traps. If it becomes 3 pages, you have lost the point.

    Return to this section

Study methods · Article 23 of 48