library(lsr)
pairedSamplesTTest(formula = ~ WB_T2 + WB_T1,
       data = prepost)

Paired samples t-test

Variables:  WB_Avg2 , WB_Avg

Descriptive statistics:
            WB_Avg2 WB_Avg difference
   mean       3.169  3.006      0.162
   std dev.   0.468  0.465      0.383

Hypotheses:
   null:        population means equal for both measurements
   alternative: different population means for each measurement

Test results:
   t-statistic:  4.892
   degrees of freedom:  132
   p-value:  <.001

Other information:
   two-sided 95% confidence interval:  [0.097, 0.228]
   estimated effect size (Cohen's d):  0.424

fit <- lm(WB_Avg ~ WB_Avg2*BF_Conscientiousness, data = prepost)
summary(fit)

Call:
lm(formula = WB_Avg ~ WB_Avg2 * BF_Conscientiousness, data = prepost)

Residuals:
     Min       1Q   Median       3Q      Max
-0.97588 -0.24927 -0.02415  0.23182  0.82299

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)
(Intercept)                  -1.16709    0.85387  -1.367  0.17406
WB_Avg2                       1.23733    0.27272   4.537 1.29e-05 ***
BF_Conscientiousness          0.62495    0.23760   2.630  0.00957 **
WB_Avg2:BF_Conscientiousness -0.17303    0.07442  -2.325  0.02162 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3397 on 129 degrees of freedom
Multiple R-squared:  0.4788,    Adjusted R-squared:  0.4667
F-statistic: 39.51 on 3 and 129 DF,  p-value: < 2.2e-16