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Biostatistics and Epidemiology as tested on USMLE Step 1, USMLE Step 2 CK, IFOM CSE, SMLE and university basic-science and clinical exams — the same core concepts (study design, bias, diagnostic testing, risk measures, hypothesis testing) show up across all of them.

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27 high-yield summary cards organized by topic, 50 practice MCQs with full explanations, and 63 active-recall flashcards — all filterable by topic so you can drill a single weak area instead of starting from scratch.

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USMLE Step 1 & 2 CK · IFOM CSE · SMLE · University Exams

Master Biostatistics in 10 minutes a day Welcome back — pick up where you left off

Free high-yield formulas, board-style MCQs with full explanations, and spaced-repetition flashcards from A Little Push Academy. No signup needed.

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Formulas

High-yield cards on NNT, bias, power, survival and more.

27 cards

Practice MCQs

Board-style vignettes with a reason for every choice.

50 questions

Flashcards

Spaced repetition that brings back what you miss.

63 cards

2×2 Calculator

Sensitivity, PPV, NNT, odds and risk ratios with 95% CIs.

3 study modes

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ALPA High-Yield Biostatistics & Epidemiology — Formula Cheat Sheet
www.alpa.academy/biostatistics-hub.html · A Little Push Academy

High-Yield Core Concepts (27 cards)

USMLE Step 1 / Step 2 CK / IFOM / SMLE

0 / 27 read

01 Must Know

Sensitivity & Specificity

Fixed intrinsic properties of a screening test. Do NOT change with population prevalence.

Sensitivity (TP Rate) A / (A + C)
Specificity (TN Rate) D / (B + D)

Mnemonic

SnNout — high Sensitivity, Negative result, rules OUT disease. SpPIn — high Specificity, Positive result, rules IN disease.

02 High Yield

PPV, NPV & Prevalence

Predictive values depend directly on disease Prevalence in the tested population.

PPV (True Positive Test) A / (A + B)
NPV (True Negative Test) D / (C + D)

As Prevalence ↑: PPV ↑ and NPV ↓.

03 Core Concept

Cut-Off Shift & ROC Curves

Shifting diagnostic cut-off points changes the trade-off between Sensitivity and Specificity.

Lowering Cut-off: Sensitivity ↑ (FP ↑), Specificity ↓. Used for screening.

Raising Cut-off: Specificity ↑ (FN ↑), Sensitivity ↓. Used for confirmation.

ROC Curve: Plots Sensitivity vs (1 - Specificity). Area Under Curve (AUC) = accuracy.

04 Calculations

Odds Ratio vs Relative Risk

Choose based on study design. Odds Ratio for retrospective, Relative Risk for prospective.

Odds Ratio (Case-Control) (A×D) / (B×C)
Relative Risk (Cohort) [A/(A+B)] / [C/(C+D)]

Rare Disease Assumption: When disease prevalence is low (<5%), OR ≈ RR.

05 Very Common

ARR, NNT & NNH

Quantifying therapeutic benefit or harm in clinical trials.

ARR = Risk_Control - Risk_Treatment

NNT = 1 / ARR | NNH = 1 / Attributable Risk (AR)

Rule: Always round NNT and NNH UP to the next integer!

06 Core Concept

Precision, SD & Confidence Intervals

Distinguishing reliability (precision) vs validity (accuracy) and estimating population parameters.

SEM = Standard Deviation / √(n)

95% CI = Mean ± 1.96 × SEM (or ± 2 × SEM)

Larger sample size (n ↑) = smaller SEM = narrower CI = increased precision.

07 Core Concept

Type I (α) & Type II (β) Errors

Errors in hypothesis decision-making and statistical power.

Type I (α): False positive (saying effect exists when null is true).

Type II (β): False negative (missing true effect). Power = 1 - β.

Power ↑ when: Sample size (n) ↑, effect size ↑, or α threshold ↑.

08 Epidemiology

Study Designs Matrix

Cross-Sectional: Snapshot at 1 point in time. Measures Prevalence.

Case-Control: Cases vs controls. Retrospective. Measures Odds Ratio.

Cohort Study: Exposed vs unexposed followed over time. Measures Incidence & RR.

09 High Yield

Choosing Statistical Tests

t-Test: Compares 2 continuous means (e.g. BP in males vs females).

ANOVA: Compares ≥ 3 continuous means (e.g. BP in 3 drug groups).

Chi-Square (χ²): Compares percentages/proportions of categorical data (large samples).

Fisher's Exact Test: Same as chi-square, but for small sample sizes.

10 Core Concept

Confounding vs Effect Modification

Distinguish third variable distortions via Stratification Analysis.

Confounding: Stratified RR/OR are equal to each other, but DIFFERENT from crude OR. (Control by matching/randomization).

Effect Modification: Stratified RR/OR are DIFFERENT from each other. (A biological phenomenon; NOT a bias!).

11 Core Concept

Clinical Trial Phases

Trials occur after preclinical animal/in vitro testing and proceed through 5 phases before and after FDA approval.

Phase 0: Microdosing PK/PD in a few volunteers. Often skipped.
Phase 1: Small group, healthy or patients. Safety & max tolerated dose.
Phase 2: Moderate group with disease. Does it Work? (efficacy + short-term AEs)
Phase 3: Large group, RCT vs standard of care. Any Improvement?
Phase 4: Postmarketing surveillance. Can it stay on the Market? (rare/long-term AEs)

Mnemonic

"Can I SWIM?" — 0, 1, 2, 3, 4.

12 Core Concept

Blinding & Crossover Trials

Trial quality improves with randomization and blinding of who knows the treatment assignment.

Double-blind: Neither subject nor researcher knows the group. Triple-blind: also blinds the analysts.

Crossover trial: Each subject receives both treatments in random order, serving as their own control, separated by a washout period.

13 High Yield

ITT vs As-Treated vs Per-Protocol

Intention-to-treat: Analyzed by original random assignment, no exclusions. "Once randomized, always analyzed" — preserves randomization but may dilute true effect.

As-treated: Analyzed by treatment actually received. ↑ risk of bias.

Per-protocol: Excludes subjects who didn't complete treatment as assigned. ↑ risk of bias.

14 Epidemiology

Bradford Hill Criteria for Causation

Necessary but not sufficient principles supporting a causal (not just associative) relationship.

Strength Consistency Specificity Temporality Biological gradient Plausibility Coherence Experiment Analogy

15 Epidemiology

Case Series, Twin, Adoption & Ecological Studies

Case series: Describes patients with the same diagnosis/treatment. No comparison group — cannot show risk-factor association.

Twin concordance study: Compares disease concordance in monozygotic vs dizygotic twins. Measures heritability ("nature vs nurture").

Adoption study: Compares traits in biological vs adoptive relatives to separate genetic from environmental influence.

Ecological study: Compares disease/risk-factor frequency across whole populations, not individuals — prone to the ecological fallacy.

16 High Yield

Likelihood Ratios (LR+ / LR-)

Combine sensitivity and specificity into one number that updates pretest probability directly.

LR+ Sens / (1 − Spec)
LR− (1 − Sens) / Spec

LR+ > 10 = highly specific test. LR− < 0.1 = highly sensitive test. Pretest odds × LR = posttest odds.

17 Core Concept

Kaplan-Meier Curve & Hazard Ratio

Kaplan-Meier estimates survival probability over time ("time-to-event" data, often mortality).

Survival probability = 1 − event probability. Significance of the difference between two curves is tested via log-rank test or Cox regression.

Hazard ratio: HR = 1 no difference; HR > 1 event more frequent in treatment/exposure group; HR < 1 protective effect.

18 Very Common

Incidence vs Prevalence

Incidence # new cases / at-risk pop, per time
Prevalence Incidence × avg. duration

Prevalence ≈ Incidence for short-duration disease (common cold). Prevalence > Incidence for chronic disease (diabetes). Prevalence ↑ PPV, ↓ NPV.

19 Core Concept

Mortality, Attack & Case Fatality Rate

Mortality rate Deaths / population, per year
Attack rate Ill people / people exposed
Case fatality rate Deaths / cases × 100

20 Epidemiology

Demographic Transition & Population Pyramids

As a country develops, birth and mortality rates decline to varying degrees, reshaping the population's age structure.

Short life expectancy + growing population = wide-based pyramid. Long life expectancy + declining population = top-heavy pyramid.

21 Must Know

Mean, Median & Mode

Mean: Sum of values / count. Most affected by outliers.

Median: Middle value (average of 2 middles if even count). Best for skewed data.

Mode: Most common value. Least affected by outliers.

22 Core Concept

Normal, Bimodal & Skewed Distributions

Normal: Mean = median = mode. 68% within 1 SD, 95% within 2 SD, 99.7% within 3 SD.

Bimodal: Suggests 2 distinct populations (e.g., fast vs slow acetylators).

Positive skew: Mean > median > mode, tail right. Negative skew: Mean < median < mode, tail left.

23 Must Know

H0, H1 & the P-Value

H0 (null): No difference/association between groups.

H1 (alternative): At least one difference/association exists.

P-value: Probability of results this extreme if H0 is true. P < 0.05 (typical α) → reject H0.

24 High Yield

Confidence Intervals

Range expected to contain the true population value, with a specified probability (CI = 1 − α).

95% CIMean ± 1.96×SE
99% CIMean ± 2.58×SE

H0 rejected when the 95% CI for a mean difference excludes 0, or for an OR/RR excludes 1.

25 Core Concept

Meta-Analysis

Pools summary data (means, RRs) from multiple studies for a more precise effect estimate, and estimates heterogeneity between studies.

Improves power, strength of evidence, and generalizability — but is limited by the quality of the included studies and selection/publication bias.

26 High Yield

Statistical vs Clinical Significance

Statistical significance: Low probability the result is due to chance.

Clinical significance: The size of the real-world impact on patient outcomes.

A study can be highly statistically significant with a clinically trivial effect size — watch for this trap on exam vignettes.

Expanded Epidemiological Biases & How to Control Them

Type of Bias	Definition & Clinical Vignette Pattern	Method to Reduce / Control
Selection / Berkson Bias	Non-random sampling or selecting hospitalized controls (Berkson bias) that don't represent the true target population. Convenience sampling (enrolling whoever is easiest to reach) is a common sub-type.	Randomization, high response rate, correct comparison group
Recall Bias	Subjects with negative outcome recall past exposures more accurately than healthy controls (common in retrospective case-control studies).	Decrease time from exposure to follow-up; use medical records as source
Measurement / Procedure Bias	Information is gathered in a systematically distorted way (e.g., a faulty automatic BP cuff), or subjects in different groups aren't treated/tested the same way (procedure bias).	Objective, standardized, pre-planned data collection; blinding
Hawthorne Effect	Study subjects alter their behavior because they know they are actively being observed ("Hawthorne watches you like a hawk").	Placebo control, unobtrusive monitoring
Observer-Expectancy (Pygmalion) Bias	Researcher's belief in a treatment's efficacy changes the outcome of that treatment — e.g., an observer expecting improvement is more likely to document a positive outcome.	Blinding (masking) of researchers analyzing outcomes
Lead-Time Bias	Early detection by screening creates false impression of prolonged survival without altering true disease trajectory or outcome.	Measure "back-end" survival adjusted for severity at diagnosis
Length-Time Bias	Screening preferentially detects slowly progressive, less aggressive cases (long latency) over rapidly fatal ones, giving an illusion of improved survival.	Randomized controlled trial of the screening program itself
Confounding	An unmeasured factor is associated with BOTH the exposure and outcome, and is not on the causal pathway (e.g., coffee drinking appears linked to lung cancer, but smoking is the true cause).	Matching, randomization, stratified/regression analysis, crossover design