Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: norm.kw$Value ~ norm.kw$Treatment + (1 | as.factor(norm.kw$Expt_no))

REML criterion at convergence: 35

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-0.9939 -0.6033 -0.1146  0.3446  1.3472 

Random effects:
 Groups                     Name        Variance Std.Dev.
 as.factor(norm.kw$Expt_no) (Intercept) 0.7719   0.8786  
 Residual                               0.7851   0.8861  
Number of obs: 12, groups:  as.factor(norm.kw$Expt_no), 6

Fixed effects:
                          Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)                24.9933     0.5094  8.0271  49.063 3.09e-11 ***
norm.kw$TreatmentFeedback  -1.5467     0.5116  5.0000  -3.023   0.0293 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
nrm.kw$TrtF -0.502
