Progress · Notebook Atlas

Progress

Where you stand, framed around the exam: what you’d likely score on the mock today, what’s due for review, and how far through each lecture you are.

Levels done: — of 225
Topics complete: — of 75
Answers so far: —
Due today: —

Exam readiness

Predicted mock mark

Reading your answers…

Review schedule

Reading your review queue…

Mastery map

Each colony is a topic. It fills a third for each level you complete, and once you’ve answered 3 questions on it a pen arc shows how many of your last 10 were right.

Not startedFirst lookWorking knowledgeAll three levelsRecent answers rightContinue hereReview today

By lecture

Levels completed out of three per topic, the share right in your recent answers, and what’s due today. Open a lecture for its topics.

Start here

F0Foundations ext0/9 done

What is data?All three levels done
Fractions, proportions, percentages and roundingAll three levels done
Reading the symbols (Σ, x̄, √, ², subscripts, ≥)Working knowledge done
Chance and probabilityAll three levels done
Averages and spread, by eyeAll three levels done
Reading a graphWorking knowledge done
Samples and populationsFirst look done
Counting in a 2×2 tableFirst look done
Comparing groups: real difference or chance?Not started

Session 2

L5Introduction to numerical concepts in research0/6 done

Data and types of variablesAll three levels done · p.3–13
Summary statisticsWorking knowledge done · p.14–25
Distribution of dataWorking knowledge done · p.26–34
Types of distributionFirst look done · p.35–46
Normal vs non-normalNot started · p.47–58
Confidence intervalsFirst look done · p.59–70

Lecture 5 view

L6Hypothesis testing and statistical tests0/11 done

What is a hypothesisWorking knowledge done · p.3–8
Test, null and alternate hypothesesFirst look done · p.9–17, 30
Alpha errorFirst look done · p.18–24
Tests, p-values, significanceNot started · p.25–33
p-value misconceptionsNot started · p.34–40
CIs and p-valuesNot started · p.41–42
Beta error and powerNot started · p.43–47
Choosing a statistical testNot started · p.48–51
One-sample t-testNot started · p.52–54
One- vs two-sided; equivalenceNot started · p.55–58
Interpreting resultsNot started · p.59–60

Lecture 6 view

Session 3

L7Systematic errors0/12 done

Study designs primerFirst look done · p.3–8, 70–71
What is systematic errorNot started · p.9–12, 72–73
ConfoundingNot started · p.13–21, 74–75
Confounder, mediator, moderatorNot started · p.22–29, 76–77
Selection biasNot started · p.30–33, 78–79
Classification of interventionsNot started · p.34–37, 80–81
Deviations from intended interventionsNot started · p.38–46, 82–85
Missing dataNot started · p.47–53
Measurement of outcomesNot started · p.54–60, 86–87
Selection of the reported resultNot started · p.61–64, 88–89
Decision flowchartsNot started · p.65–66
Risk-of-bias toolsNot started · p.67–68

Lecture 7 view

L8Study design0/7 done

ClassificationFirst look done · p.3–8
Cross-sectional studiesNot started · p.9–19, 92–97
Cohort studiesNot started · p.20–30, 98–101, 104–105, 110–111
Case-control studiesNot started · p.31–48, 102–103
Randomised controlled trialsNot started · p.49–74, 106–109
Quasi-experimental designsNot started · p.75–85
Design toolsNot started · p.86–90

Lecture 8 view

L9Variations in study design0/8 done

Parallel and multi-arm trialsNot started · p.3–6, 10, 17–18, 38–39
Factorial trials and interactionsNot started · p.7–20, 40–41
Cluster and stepped-wedgeNot started · p.21–24, 42–43
Cross-overNot started · p.25–26
Dose escalation (3+3)Not started · p.27
Pilot vs feasibilityNot started · p.28–30, 44–47
Non-inferiority and equivalenceNot started · p.31–32
Split-part, adaptive, cohort-multiple RCTNot started · p.33–36

Lecture 9 view

Session 4

L10Sample size calculations0/6 done

Why sample size mattersNot started · p.3–5, 17–18
Continuous outcomesNot started · p.6–7
Minimal important differenceNot started · p.8–9
Binary outcomesNot started · p.10
No previous studyNot started · p.7, 11–12, 19–20
Power calculationsNot started · p.13–14, 17–18

Lecture 10 view

L11Graphical representation of data and measures of association0/8 done

Summary measures by data typeFirst look done · p.3, 6–8
TablesNot started · p.9–12
Graphs for categorical dataNot started · p.13–17
Graphs for quantitative dataNot started · p.18–21, 25–26
Scatter plots and correlationNot started · p.22–24, 39
Risk, odds, RR, OR, RD, NNTNot started · p.28–34, 42–47
Other outcome typesNot started · p.35–38, 48–49
Pearson vs SpearmanNot started · p.39, 50–51

Lecture 11 view

L12Introduction to measurement errors and diagnostic test accuracy studies0/8 done

Accuracy vs precisionNot started · p.4–11, 53–54
Random vs systematic errorNot started · p.12–17, 55–56
Assessing measurement errorNot started · p.18–24, 36, 50
Diagnostic test accuracy (2×2)Not started · p.24–30, 57–60
Thresholds and ROC curvesNot started · p.31–32
Agreement: kappaNot started · p.23, 33–35, 61–62
Bland–Altman and ICCNot started · p.36–39
Surrogate outcomesNot started · p.40–49

Lecture 12 view

By misconception

The slips behind your wrong answers, most frequent first, with the topics they turned up in.

Reading your answers…