<SortableTree
onlyExpandSearchedNodes
canDrag={false}
treeData={[{
title: 'Type of Data',
children: [{
title: 'Categorical',
children: [{
title: 'Chi-Square Test',
subtitle: 'Test for independence for two variables or goodness-of-fit test'
}]
},
{
title: 'Continuous',
children: [{
title: 'Group Differences',
children: [{
title: 'Between Means',
children: [{
title: 'How many groups?',
children: [{
title: 'Two',
children: [{
title: 'Are assumptions satisfied (potentially after data transformations)?',
subtitle: '1. Equal variances in both groups, 2. Data normally distributed or sufficiently large sample',
children: [{
title: 'If yes, can use a parametric test',
subtitle: 'Student\'s t-test (paired or unpaired)'
},
{
title: 'If not, use a nonparametric randomization test',
subtitle: 'E.g., Wilcoxon Rank Sum Test or Mann-Whitney U Test'
}
]
}]
},
{
title: 'More than two',
children: [{
title: 'Are assumptions satisfied (potentially after data transformations)?',
subtitle: '1. Equal variances in each group, 2. Data normally distributed or sufficiently large sample',
children: [{
title: 'If yes, can use a parametric test',
subtitle: 'ANOVA (Analysis of Variance)'
},
{
title: 'If not, use a nonparametric randomization test',
subtitle: 'E.g., Kruskal-Wallis Test'
}
]
}]
}
]
}]
},
{
title: 'Between Variances',
subtitle: 'Bartlett\'s Test or Levene\'s Test when data are normally distrubuted'
}
]
},
{
title: 'Relationships between Variables',
children: [{
title: 'Regression Analysis',
subtitle: 'Simple linear regression for one response and one predictor,\nmultiple regression in case of several explanatory variables.'
}]
}
]
}
]
}]}
/>