independent-sample T Test

data structure :

 group 1 2 subjects 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 data 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3

it means subject are independent , you record the data from every subject with the same measurements under different condition.

here we should use  indepensampleT in FT

dependent sample T Test

data structure :

 subjects 1 2 3 4 5 6 7 8 Pre 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 Post 0.4 0.5 0.6 0.3 0.2 0.1 0 -0.1

we call  this  paired samples T test,  you could check that the data pre and post come from the same subject under differetn measure

here we should use  depensampleT in FT

Anova

The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups.

For example, a researcher wishes to know whether different pacing strategies affect the time to complete a marathon. The researcher randomly assigns a group of volunteers to either a group that (a) starts slow and then increases their speed, (b) starts fast and slows down or (c) runs at a steady pace throughout. The time to complete the marathon is the outcome (dependent) variable.

`indepsamplesF`

data structure :

 subject/group group1 group2 group3 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 tdcs 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1

MAnova(more than two way)

two way repeated measure anova

`depsamplesFmultivariate`

data structure :

 subject/group group1 group2 group3 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 tdcs ad 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 fu 0.4 0.5 0.6 0.3 0.2 0.1 0 -0.1 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 tacs ad 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 0.3 0.4 0.5 0.6 0.4 0.3 0.2 0.1 fu 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3 sham ad 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3 fu 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3 0.2 0.3 0.4 0.5 0.6 0.5 0.4 0.3