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Proportions
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Independent samples
(Unpaired in case of two)
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Dependent samples
(Paired in case of two)
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| 2 proportions
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- [math]\displaystyle{
\begin{align}
z & = \frac{p_1-p_2}{SE_{pooled(p_1-p_2)}} \\
& = \frac{p_1-p_2}{\sqrt{\frac{\bar{p}(1-\bar{p})}{n_1}+\frac{\bar{p}(1-\bar{p})}{n_2}}}
\end{align}
}[/math]
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| ≥ 3 proportions
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Enough large sample
- [math]\displaystyle{ \chi^2 }[/math] test
- [math]\displaystyle{ \chi^2 = \sum \frac{(O - E)^2}{E} }[/math]
- [math]\displaystyle{ O }[/math] = observed values
[math]\displaystyle{ E }[/math] = expected values
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- McNemar's [math]\displaystyle{ \chi^2 }[/math] test
- [math]\displaystyle{
\begin{align}
& McNemar's\ \chi^2 \\
& = \frac{(n_1-n_2)^2}{n_1+n_2}
\end{align}
}[/math]
- [math]\displaystyle{ n_i }[/math] = number of observations in discordant pair
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Testing linear association
- [math]\displaystyle{ \chi^2 }[/math] trend test
- [math]\displaystyle{
\begin{align}
& \chi^2 trend \\
& = \frac{(\bar{x_1}-\bar{x_2})^2}{s^2(\frac{1}{n_1}+\frac{1}{n_2})} \\
& s = \sqrt{\sum \frac{(x_i-\bar{x_i})^2}{n-1}}
\end{align}
}[/math]
- [math]\displaystyle{ x_i }[/math] = weighted values
- [math]\displaystyle{ n_i }[/math] = number of observations
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| ≥1 cell expected value <5
Fisher's exact test
- very rare in real researches
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Means
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Parametric
i.e., normally distributed
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Non-parametric
i.e., not normally distributed
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Independent samples
(Unpaired in case of two)
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Dependent samples
(Paired in case of two)
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Independent samples
(Unpaired in case of two)
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Dependent samples
(Paired in case of two)
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| 2 means
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Enough large sample
- [math]\displaystyle{
\begin{align}
z & = \frac{\bar{x_1}-\bar{x_2}}{SE_{(\bar{x_1}-\bar{x_2})}} \\
& = \frac{\bar{x_1}-\bar{x_2}}{\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}}
\end{align}
}[/math]
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- [math]\displaystyle{
\begin{align}
paired\ t & = \frac{\bar{d}}{SE_d} \\
& = \frac{\bar{d}}{\frac{s}{\sqrt{n}}} \\
\end{align}
}[/math]
- where [math]\displaystyle{ \bar{d} }[/math] is the mean of differences of paired observations
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- Wilcoxon signed rank test
- here 'signed' means 'take into account signs of differences of paired data'
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Small sample size <30 in a group
- [math]\displaystyle{
\begin{align}
t & = \frac{\bar{x_1}-\bar{x_2}}{SE_{(\bar{x_1}-\bar{x_2})}} \\
& = \frac{\bar{x_1}-\bar{x_2}}{\sqrt{\frac{(n_1-1)s_1^2+(n_2-1)s_2^2}{(n_1-1)+(n_2-1)}}\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}}
\end{align}
}[/math]
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Large discrepancy in SDs between groups
- Bootstrap
- Non-parametric
- Fisher-Behrens
- Welch
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| ≥ 3 means
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- Linear-regression model
- Repeated measures ANOVA
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*needs try to transform data into parametric (e.g., logarithmic), or other considerations
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