Title: Big Ol' Bonewizardry Deck / Hex Clues / Biostats
Tags: hex-clues hex-clues::biostats

Q: 1 standdar deviation
A: 1 SD 🎯
68% of values within ±1 SD 🎯
normal distribution 🔔

Q: 2 standard deviations
A: 2 SD 🎯
95% of values within ±2 SD 🎯
significance gate 🚪

Q: What is the Mean?
A: arithmetic average (sum / n) 🎯
sensitive to outliers 🎯

Q: 3 standard deviations
A: 3 SD 🎯
99.7% of values within ±3 SD 🎯
outlier territory 🌌

Q: What is the Median?
A: middle value when ordered 🎯
preferred for skewed data 🎯

Q: What is the Mode?
A: most frequent value 🎯
used for categorical / nominal 🏆

Q: What is Clinical wisdom?
A: I think... paper 🎯
expert opinion, lowest evidence 📉

Q: What is a Case Report?
A: single unusual patient 🎯
hypothesis-generating, no controls 🎯

Q: Typically used in cohort studies
A: Relative Risk (RR) 🎯
incidence exposed / unexposed 🎯
cohort studies 🎯

Q: Typically used in case-control studies
A: Odds Ratio (OR) 🎯
odds exposure in cases vs controls 🎯
case-control studies 🔍

Q: What does a Null hypothesis tell you?
A: no difference / association 🎯
reject if p ❌

Q: What is a Case Series report?
A: several unusual patients 🎯
no control group 📋

Q: What is a Meta-analysis?
A: pools data from multiple trials 🎯
highest level of evidence ⛰️

Q: What is a Cross Sectional study?
A: disease + exposure at one time 🎯
gives prevalence 🕐

Q: What is NNT?
A: 1 / ARR 🎯
patients treated to prevent 1 bad outcome 🛡️

Q: What are Consensus Panels?
A: experts vote 🎯
panel recommendation 🪧

Q: What does Power tell you?
A: 1 - β 🎯
probability of detecting a true effect 🎯
typically ≥0.80 🎯

Q: Systematic error arriving from inaccurate measurement
A: Measurement bias 🎯
inaccurate measurement tool 🎯
systematic error 📊

Q: A selection bias due to differing rates of hospitalization
A: Berkson bias 🎯
selection from hospitalized patients 🎯
skewed sample 📊

Q: Pt fails to recall accurate information about exposure history
A: Recall bias 🎯
memory failure on exposure 🎯
case-control bug 📉

Q: The participant, who knows that he is under supervision, does better
A: Hawthorne effect 🎯
subjects know being observed 🎯
perform better when watched 🎭

Q: What does a p value <0.5 tell you?
A: p 🎯
1 in 20 wrong by chance 🎯
statistically significant ✅

Q: determination of prevalence of exposure and disease at a specific time
A: Cross-sectional study 🎯
snapshot at one time 🎯
prevalence study 🕐

Q: Systematic difference between those who do and do not participate in the study
A: Response bias 🎯
non-response bias 🎯
participants ≠ non-participants 📊

Q: What does a Confidence Interval = 95% tell you?
A: 95% CI 🎯
95% sure true value in interval 🎯
excludes null (1 or 0) = significant ✅

Q: The negative effects of a disease may take years to become clinically apparent
A: Latent period 🎯
delay before clinical effect 🎯
slow burn 🕰️

Q: assessment of a link between exposure and an outcome has 2 names
A: Ecological / correlation study 🎯
population-level link 🎯
exposure-outcome 🔗

Q: report of disease or treatment that is complied by aggregating several similar people
A: Case series 🎯
aggregated similar cases 🎯
no control group 📋

Q: What is NNH?
A: 1 / AR 🎯
exposures per 1 patient harmed 💀

Q: What is a Case Control study?
A: retrospective; prevalence (total cases) 🎯
uses Odds Ratio 🎯

Q: Early detection of diagnosis test or screening test causes false increase in survival time
A: Lead time bias 🎯
earlier detection ≠ longer life 🎯
false survival increase 🎭

Q: T Test
A: compares means between 2 groups 🎯
IV nominal / ordinal, DV continuous 🎯

Q: Chi Square
A: compares proportions between 2+ groups 🎯
both IV + DV nominal / ordinal 🎲

Q: What does Prevalence tell you?
A: existing cases / people at risk 🎯
point in time snapshot 📸

Q: What is a type II error?
A: fail to reject false null 🎯
false negative + too pessimistic 😴

Q: ANOVA
A: compares means between 2+ groups 🎯
IV nominal / ordinal, DV continuous 🎯

Q: Absolute risk reduction (ARR)
A: risk difference (intervention - control) 🎯
not proportional 🎯

Q: What does Incidence tell you?
A: new cases / people at risk 🎯
rate per unit time ⏰

Q: What is a type I error?
A: reject true null 🎯
false positive + too optimistic 😊

Q: Pearson Correlation
A: relationship between 2 continuous variables 📡🔬
study time vs exam score 📚

Q: Observer belief of the efficacy of a drug causes a falsely good report from the study
A: Pygmalion effect 🎯
observer expectation skews outcome 🎯
form of observer bias 🎭

Q: What does Specificity tell you?
A: TN / (TN+FP) 🎯
SpPin: rules IN if positive ✅