This is an example of an experiment in which a treatment group is measured before and after the treatment. Also, a control group is measured before and after the placebo treatment. This design will allow estiamting the effect of a placebo and estimating the treatment effect after considering the effect of the placebo.

Data will be generated according to the parameters selected. You can fix the mean and standard deviation in before and after the intervention for both the control (that is treated by a placebo) and the treatment group (that gets the treatment to evaluate)

We evaluate if the data in control and treatment groups are comparable in means at the initial conditions. In R:

`t.test(Initial~Group,data=df)`

`res <- t.test(Initial~Group,data=df)`

`round(res$conf.int,2)`

`df <- df %>% filter(Group=='Control')`

`with(df,t.test(dif))`

`df <- df %>% filter(Group=='Control')`

`res <- with(df,t.test(dif))`

`round(res$conf.int,2)`

`df <- df %>% filter(Group=='Trata')`

`with(df,t.test(dif))`

`df <- df %>% filter(Group=='Trata')`

`res <- with(df,t.test(dif))`

`round(res$conf.int,2)`

`t.test(dif~Group,data=df)`

`res <- t.test(dif~Group,data=df)`

`round(res$conf.int,2)`