# Blog tags

- significance 11
- power 13
- simplicity 9
- R 34
- breakpoint regression 1
- multiple comparisons 5
- silly tests 4
- graphics 14
- effect sizes 11
- measurement error 5
- design features 10
- organisation 4
- logistic regression 4
- correlational studies 3
- mixed-effects models 5
- cluster-randomised experiments 2
- generalised additive models 3
- non-linearities 5
- missing data 1
- open science 2
- correction 1
- multiple regression 3
- reading 1
- tutorial 7
- machine learning 1
- random forests 1
- R tip 1
- bootstrapping 1
- replication 2
- predictive modelling 1
- assumptions 1

## significance

- Confidence interval-based optional stopping
- The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
- On correcting for multiple comparisons: Five scenarios
- Experiments with intact groups: spurious significance with improperly weighted t-tests
- Analysing experiments with intact groups: the problem and an easy solution
- Power simulations for comparing independent correlations
- Explaining key concepts using permutation tests
- Assessing differences of significance
- A purely graphical explanation of p-values
- Calibrating p-values in 'flexible' piecewise regression models
- Analysing pretest/posttest data

## power

- Increasing power and precision using covariates
- Abandoning standardised effect sizes and opening up other roads to power
- On correcting for multiple comparisons: Five scenarios
- Causes and consequences of unequal sample sizes
- The problem with cutting up continuous variables and what to do when things aren't linear
- Analysing experiments with intact groups: the problem and an easy solution
- Covariate adjustment in logistic mixed models: Is it worth the effort?
- Covariate adjustment in logistic regression — and some counterintuitive findings
- Silly significance tests: Tests unrelated to the genuine research questions
- Power simulations for comparing independent correlations
- More on why I don't like standardised effect sizes
- The curious neglect of covariates in discussions of statistical power
- Analysing pretest/posttest data

## simplicity

- Common-language effect sizes
- Surviving the ANOVA onslaught
- Silly significance tests: The main effects no one is interested in
- Silly significance tests: Tests unrelated to the genuine research questions
- A more selective approach to reporting statistics
- Overaccuracy and false precision
- Silly significance tests: Tautological tests
- Silly significance tests: Balance tests
- Analysing pretest/posttest data

## R

- Checking model assumptions without getting paranoid
- Confidence interval-based optional stopping
- Creating comparable sets of stimuli
- Interactions between continuous variables
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Confidence intervals for standardised mean differences
- Some illustrations of bootstrapping
- What data patterns can lie behind a correlation coefficient?
- Common-language effect sizes
- Tutorial: Drawing a dot plot
- R tip: Ordering factor levels more easily
- Classifying second-language learners as native- or non-nativelike: Don't neglect classification error rates
- Tutorial: Drawing a boxplot
- Tutorial: Drawing a line chart
- Tutorial: Drawing a scatterplot
- Why reported R² values are often too high
- Experiments with intact groups: spurious significance with improperly weighted t-tests
- Drawing a scatterplot with a non-linear trend line
- Causes and consequences of unequal sample sizes
- The problem with cutting up continuous variables and what to do when things aren't linear
- Analysing experiments with intact groups: the problem and an easy solution
- Covariate adjustment in logistic mixed models: Is it worth the effort?
- Covariate adjustment in logistic regression — and some counterintuitive findings
- Some tips on preparing your data for analysis
- Power simulations for comparing independent correlations
- Explaining key concepts using permutation tests
- Thinking about graphs
- Some alternatives to bar plots
- Assessing differences of significance
- Silly significance tests: Balance tests
- A purely graphical explanation of p-values
- Calibrating p-values in 'flexible' piecewise regression models
- Analysing pretest/posttest data

## breakpoint regression

## multiple comparisons

- The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
- Why reported R² values are often too high
- On correcting for multiple comparisons: Five scenarios
- Silly significance tests: Tests unrelated to the genuine research questions
- Calibrating p-values in 'flexible' piecewise regression models

## silly tests

- Silly significance tests: The main effects no one is interested in
- Silly significance tests: Tests unrelated to the genuine research questions
- Silly significance tests: Tautological tests
- Silly significance tests: Balance tests

## graphics

- Checking model assumptions without getting paranoid
- Interactions between continuous variables
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- What data patterns can lie behind a correlation coefficient?
- Tutorial: Drawing a dot plot
- R tip: Ordering factor levels more easily
- Tutorial: Drawing a boxplot
- Tutorial: Drawing a line chart
- Tutorial: Drawing a scatterplot
- Drawing a scatterplot with a non-linear trend line
- A more selective approach to reporting statistics
- Thinking about graphs
- Some alternatives to bar plots

## effect sizes

- Abandoning standardised effect sizes and opening up other roads to power
- Confidence intervals for standardised mean differences
- Which predictor is most important? Predictive utility vs. construct importance
- What data patterns can lie behind a correlation coefficient?
- Common-language effect sizes
- Why reported R² values are often too high
- Covariate adjustment in logistic mixed models: Is it worth the effort?
- Covariate adjustment in logistic regression — and some counterintuitive findings
- More on why I don't like standardised effect sizes
- A more selective approach to reporting statistics
- Why I don't like standardised effect sizes

## measurement error

- Abandoning standardised effect sizes and opening up other roads to power
- Which predictor is most important? Predictive utility vs. construct importance
- Controlling for confounding variables in correlational research: Four caveats
- More on why I don't like standardised effect sizes
- Why I don't like standardised effect sizes

## design features

- Consider generalisability
- Suggestions for more informative replication studies
- Increasing power and precision using covariates
- Confidence interval-based optional stopping
- Creating comparable sets of stimuli
- Abandoning standardised effect sizes and opening up other roads to power
- Experiments with intact groups: spurious significance with improperly weighted t-tests
- Causes and consequences of unequal sample sizes
- Analysing experiments with intact groups: the problem and an easy solution
- Explaining key concepts using permutation tests

## organisation

- Creating comparable sets of stimuli
- Automatise repetitive tasks
- The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
- Some tips on preparing your data for analysis

## logistic regression

- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Covariate adjustment in logistic mixed models: Is it worth the effort?
- Covariate adjustment in logistic regression — and some counterintuitive findings

## correlational studies

- Which predictor is most important? Predictive utility vs. construct importance
- What data patterns can lie behind a correlation coefficient?
- Controlling for confounding variables in correlational research: Four caveats

## mixed-effects models

- Consider generalisability
- Suggestions for more informative replication studies
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Covariate adjustment in logistic mixed models: Is it worth the effort?

## cluster-randomised experiments

- Experiments with intact groups: spurious significance with improperly weighted t-tests
- Analysing experiments with intact groups: the problem and an easy solution

## generalised additive models

- Increasing power and precision using covariates
- Interactions between continuous variables
- The problem with cutting up continuous variables and what to do when things aren't linear

## non-linearities

- Increasing power and precision using covariates
- Interactions between continuous variables
- What data patterns can lie behind a correlation coefficient?
- Drawing a scatterplot with a non-linear trend line
- The problem with cutting up continuous variables and what to do when things aren't linear

## missing data

## open science

- Some advantages of sharing your data and code

## correction

## multiple regression

- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Why reported R² values are often too high

## reading

## tutorial

- Checking model assumptions without getting paranoid
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Tutorial: Drawing a dot plot
- Tutorial: Drawing a boxplot
- Tutorial: Drawing a line chart
- Tutorial: Drawing a scatterplot

## machine learning

## random forests

## R tip

## bootstrapping

## replication

- Suggestions for more informative replication studies
- Draft: Replication success as predictive utility