Levene's Test and residuals vs. fitted plot lead to different interpretations about heteroscedasticity I am performing a one-way ANOVA in R with the following data:




Cu
Day
CC




Cu1
49
30934500


Cu1
49
26860125


Cu1
49
46524750


Cu10
49
15272561


Cu10
49
31601659


Cu10
49
17627634


Cu100
49
3718127


Cu100
49
4941416


Cu100
49
6230801




The ANOVA aims to determine if CC changes (on average) depending on Cu (three levels: Cu1, Cu10, and Cu100) or, in other words, to check if Cu exerts an effect on CC.
The code I used was:
    analisis_varianza <- aov(CC~Cu, data=dia_49)
    summary(analisis_varianza)

And, to check the homoscedasticity assumption:
   leveneTest(CC~Cu, data=dia_49)

The problem is that Levene's test suggests that variances are homogeneous (p=0.5424), while the residuals vs fitted plot for the ANOVA clearly indicates that the variance is not homogenous:

On which should I rely on and why?

In general, it's a bad idea to rely on tests of normality for taking decisions. In this case, the Levene's test p-value is probably high because you have a very small sample size. On the other hand, if you have very large sample sizes, the test will show significant deviations from normality even when it's not a problem for inference.
In short, graphs are much more informative than tests.
Here's a very useful thread: Is normality testing 'essentially useless'?
