## Paired Samples T-Test

The Student’s paired samples t-test (sometimes called a dependent-samples t-test) is used to test the null hypothesis that the difference between pairs of measurements is equal to zero. A low p-value suggests that the null hypothesis is not true, and that the difference between the measurement pairs is not zero.

The Student’s paired samples t-test assumes that pair differences follow a normal distribution – in the case that one is unwilling to assume this, the non-parametric Wilcoxon signed-rank can be used in it’s place (However, note that the Wilcoxon signed-rank has a slightly different null hypothesis; that the two groups of measurements follow the same distribution).

### Arguments

 data the data as a data frame pairs a list of lists specifying the pairs of measurement in data students TRUE (default) or FALSE, perform Student's t-tests bf TRUE or FALSE (default), provide Bayes factors bfPrior a number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors wilcoxon TRUE or FALSE (default), perform Wilcoxon signed rank tests hypothesis 'different' (default), 'oneGreater' or 'twoGreater', the alternative hypothesis; measure 1 different to measure 2, measure 1 greater than measure 2, and measure 2 greater than measure 1 respectively norm TRUE or FALSE (default), perform Shapiro-wilk normality tests qq TRUE or FALSE (default), provide a Q-Q plot of residuals meanDiff TRUE or FALSE (default), provide means and standard errors ci TRUE or FALSE (default), provide confidence intervals ciWidth a number between 50 and 99.9 (default: 95), the width of confidence intervals effectSize TRUE or FALSE (default), provide effect sizes ciES TRUE or FALSE (default), provide confidence intervals for the effect-sizes ciWidthES a number between 50 and 99.9 (default: 95), the width of confidence intervals for the effect sizes desc TRUE or FALSE (default), provide descriptive statistics plots TRUE or FALSE (default), provide descriptive plots miss 'perAnalysis' or 'listwise', how to handle missing values; 'perAnalysis' excludes missing values for individual dependent variables, 'listwise' excludes a row from all analyses if one of its entries is missing

### Returns

A results object containing:

 results\$ttest a table results\$norm a table results\$desc a table results\$plots an array of groups

Tables can be converted to data frames with asDF or as.data.frame(). For example:

results\$ttest\$asDF

as.data.frame(results\$ttest)

Elements in arrays can be accessed with [[n]]. For example:

results\$plots[] # accesses the first element