XClose

UCL Great Ormond Street Institute of Child Health

Home

Great Ormond Street Institute of Child Health

Menu

A - Z index

A
B
C

Case-control study (planning a study)
Categorical data (summarising data)
Categorical data (making inferences)
Categorical data (paired data)
Categorical differences (quantifying)
Categorical variables
Centre and spread measures (summarising data)
Changing the form of data
Chi-square test (significance):
 > Fishers exact test (non-parametric)
Choice of comparison groups
Cluster randomisation (planning a study)
Cohort study (planning a study)
Comparing two groups (non-parametric)
Comparing more than two proportions (>Two comparison groups or times)
Comparison groups - more than two
Comparisons between groups (planning a study)
Comparisons of several groups (displaying results)
Competing study designs (planning a study)
Confidence intervals (making inferences):
 > For a single median (non-parametric)
 > For other population parameters (making inferences)
 > One sample: small samples and extreme proportions (making inferences)
 > Relationship between confidence intervals and significance tests
 > Two sample: small samples and extreme proportions (making inferences)
Confidence intervals when standard error cannot be estimated (making inferences)
Confounding factors (planning a study)
Contingency table - two variables (graphical display of data)
Contingency tables: more than two variables (graphical display of data)
Continuous variables
Control groups (planning a study)
Correlation coefficient r - standard error (making inferences)
Correlation - spearman (nonparametric)
Crossover trial (paired data)
Crossover trial (planning a study)
Cross-sectional study (planning a study)

D

Data assumptions (making inferences)
Data display
Data: ranking (non-parametric)
Data storage
Data types:
 > Categorical variables
 > Changing the form
 > Data types practical
 > Numeric variables
Defining and documenting the research question
Descriptive statistics (summarising data)
Difference between two means: standard error (making inferences)
Difference between two percentages: standard error (making inferences)
Difference between two proportions: standard error (making inferences)
Differences (quantifying)
Discrete variables
Displaying data:
 > More than two variables
 > One variable
 > Two variables
Displaying results:
 > Bar charts of means within each group
 > Bar charts of means within each group with error bars added
 > Boxplots
 > Comparisons of several groups
 > Displaying results alone
 > Displaying results practical
 > Serial measurements over time
 > Summary
 > Superimposing results on data
 > Displaying serial measures
Distribution: non-normal data:
 > J-shaped distributions
 > Testing normality
 > Transformations
Distribution - the normal distribution
Distribution - normal tables
Dot diagrams (graphical display of data)
Double blind study (planning a study)

E
F
G
H
I
J
K
L
M

Making inferences:
 > Categorical data
 > Confidence intervals
 > Confidence intervals for other population parameters
 > Confidence intervals when standard error cannot be estimated
 > Making inferences practical
 > Numeric data
 > One sample: small sample and extreme proportions
 > Standard error Standard error for other population parameters
 > Two sample: small sample and extreme proportions
 > What a sample tells us
Matching (comparison groups)
Mean (summarising data):
 > Bar charts of means in each group (displaying results)
 > Bar charts of means in each group with error bars added (displaying results)
 > Standard error for difference between two means (making inferences)
 > Standard error for a single mean (making inferences)
Median (summarising data)
 > Confidence intervals for a single median (nonparametric)
Measures of centre (summarising data)
 > Comparing the mean and median
Mean
Median
 > Which to use: mean or median
Measures of centre and spread (summarising data)
Measures of spread (summarising data):
 > Comparing the range, interquartile range and standard deviation
 > Interquartile range
 > Range
 > Standard deviation
Minimisation (planning a study)
More than two comparison groups and/or times:
 > Analysis of serial measurements
 > Displaying serial measurements
 > Comparing more than two proportions
 > One-way analysis of variance
 > Ordered categories
 > More than two groups practical
 > Serial measures
 > Which test
More than two variables (displays of data)
 > Contingency tables
 > Scatterplots
 > Side-by-side and stacked bar charts

 
 
N
O
P

p-values
Paired data: > Analysis of crossover trail data
 > Categorical data
 > Numeric data
 > Paired binary data: small samples and extreme proportions
 > Paired data practical
Paired or unpaired?
Paired t-test
Paired binary data: small samples and extreme proportions
Parametric significance tests
Percentages (summarising data): > Chi-square test for percentages (significance)
 > One sample proportion/percentage test (significance)
 > Standard error for difference between two percentages (making inferences)
 > Standard error for difference between two proportions (making inferences)
Pie charts (graphical display of data)
Pilot studies (planning a study)
Plan of investigation
Planning a study: > Blinding
 > Comparisons between groups
 > Competing study designs
 > Confounding factors
 > Control groups
 > Defining and documenting the research question
 > Intention-to-treat analysis
 > Losses to follow up
 > Pilot studies
 > Refusals to participation
 > Random allocation
 > Removal of confounding
 > Sample size
 > Target and sample populations
 > Types of study
 > Written protocol
Populations
Practical 1 (statistics in medical research)
Practical 2 (types, storage and graphical display of data)
Practical 3 (summarising data)
Practical 4 (quantifying differences and associations)
Practical 5 (making inferences)
Practical 6 (significance tests)
Practical 7 (paired data)
Practical 8 (non-parametric)
Practical 9 (reliability and validity)
Practical 10 (more than two groups and/or times)
Practical 11 (displaying results)
Practical 12 (evaluation - true or false)
Practical 12 (evaluation - best of five)
Practical 12 (evaluation - extended matching)
Practical 12 (evaluation - longer exercises)
Proportions (summarising data): > Chi square test for proportions (significance)
 > Comparing more than two proportions (>two comparison groups/times)
 > One sample: small samples and extreme proportions (making inferences)
 > One sample proportions/percentage test (significance)
 > Paired binary data: small samples and extreme proportions (paired data)
 > Standard error for difference between two proportions (making inferences)
 > Standard error for single proportion (making inferences)
 > Two samples: small samples and extreme proportions (making inferences)
Prospective study (planning a study)

Q
R
S

Sample - what a sample tells us (making inferences)
Sample size (planning a study)
Samples to populations (making inferences)
Scatterplot - two variables (graphical display of data)
Scatterplot - more than two variables (graphical display of data)
Serial measurements - analysis (>two comparison groups or times)
Serial measurements - display (>two comparison groups or times)
Serial measurements over time (displaying results)
Serial measures (>two comparison groups or times)
Side-by-side bar chart - two variables (graphical display of data)
Side-by-side bar chart - more than two variables (graphical display of data)
Sign test (non-parametric)
Significance level
Significance tests:
 > Alternative significance tests
 > 1- or 2-sided p-value
 > Chi-square test
 > Level of significance
 > Null hypothesis
 > One sample t-test
 > Other parametric significance tests
 > p-values
 > Relationship between confidence intervals and significance tests
 > Significance tests practical
 > Transformations (for RR, OR, r)
 > Two sample (unpaired) t-test
 > Validity of tests
Simple randomisation (planning a study)
Single blind study (planning a study)
Skew and skewed distributions (summarising data)
Spearman's correlations (non-parametric)
Spread and centre measures (summarising data)
Square and square root (summarising data)
Stacked bar charts - two variables (graphical display of data)
Stacked bar charts- more than two variables (graphical display of data)
Standard deviation (summarising data)
Standard error (making inferences)
Standard error for other population parameters (making inferences):
 > Confidence intervals when standard error cannot be estimated
 > Correlation coefficient r
 > Difference between two means
 > Difference between two percentages
 > Difference between two proportions
 > Odds ratio
 > Relative risk
 > Single mean
 > Single percentage
 > Single proportion
Statistical inference (making inferences)
Stratification (planning a study)
Summarising categorical data (summarising data)
Summarising data:
 > Comparing the mean and median
 > Comparing the range, interquartile range and standard deviation
 > Mean
 > Median
 > Non-normal data
 > Non-normal data - J-shaped distribution
 > Non-normal data - testing normality
 > Non-normal data - transformations
 > Normal distribution
 > Normal tables
 > Summarising data practical
 > Which to use: mean or median
Summarising numeric data (summarising data)
Superimposing results on data (displaying results)
Symmetric distribution (summarising data)
Systematic assignment (planning a study)

T

T-tests:
 > One-sample t-test
 > Two-samples t-test
 > Paired samples t-test
 > Paired or unpaired? (Paired data)
Target and sample populations (planning a study)
Testing normality (summarising data)
Three dimensional bar charts (graphical display of data)
Transformations (significance)
Transformations of non-normal data (summarising data)
Two groups - comparing:
 > Two samples t-test (significance tests)
 > Two samples Mann-Whitney U test (nonparametric)
 > Two groups chi squared test (significance tests)
 > Two groups fisher's exact test (nonparametric)
 > Two samples small samples and extreme proportions (inferences)
Two variables (graphical display of data):
 > One numeric and one categorical
 > Two categorical variables
 > Two numeric variables
 > Summary of displays for two variables
Types, storage and graphical display of data:
 > Categorical variables
 > Changing the form
 > Data storage
 > Graphical display of data
 > Graphical display - more than two variables
 > Graphical display - one variable
 > Graphical display - two variables
 > Numeric variables
 > Types of data
 > Types, storage and graphical display of data practical
Types of study (planning a study)

U
V
W
XYZ

There's nothing to see here.... 

....But, there's plenty of other courses on our main website