Coefficient of variation in inferential statistics

As part of an appraisal of an article ( and among other questions I have been asked to "explain the rationale for the following inferential statistical tests: paired and independent samples t-tests, coefficients of variation."

I have been routing through all my literature on statistics and searched the internet for answers all day but I could't come up with anything useful to me. The only article I found on coefficient of variation in inferential statistics was from 2009 about the variation of length between male and female green lynx spiders. It left me nowhere.

The question implies that both, t-tests (paired and independent) as well as coefficient of variation were used in the data analysis presented in the article. The methodology however states:

"Power calculations found that a sample size of 22 participants per group was required to detect a 20% (SD 31%) reduction in the PD falls risk score in the exercise group compared with the control group (power = 0.8, alpha = 0.05, correlation with covariate = 0.7), allowing for a 10% drop-out rate.
Linear regression analysis adjusted for baseline scores (ANCOVA) was used to determine if there were differences in outcome measures between the exercise and control groups at the end of the 6-month intervention period. Categorical data were dichotomized and between-group differences were compared using logistic regression models."

I have been wrecking my head over this for far too long and I cannot put Cv, t-tests (paired and independent), linear regression and logistic regression into one context, or at least understand how the former two fit into any of the latter two..

Another point I cannot get my head around is: "between group mean difference = -7%, 95% CI -20 to 5, P = 0.26" What does 95% CI (confidence interval I suppose) -20 to 5 mean? Especially the -20 to 5 is something I don't get.

Can anyone please point me in the right direction before I go crazy?