My problem is that I can see in the graph of conditional_effects and in the graph of probabilities of getting a response correct, that there is a time effect. But the output of the model says there is no effect.

time: 0, 1, 24 hrs

group: younger and older

```
model30 <- brm(stringent ~ 1 + time + group
+ time:group
+ ( 1 + time | id )
+ ( 1 + time + group | item ),
data = data30,
family = bernoulli(),
iter = 4000,
file = "model30 "
)
```

I ran a model to figure out if older people forget faster than younger people. Here is are the results of the model:

```
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 2.74 0.43 1.94 3.65 1.00 1726 3176
delayhour -1.40 0.32 -2.05 -0.79 1.00 3141 4444
delayday -2.21 0.36 -2.93 -1.52 1.00 2574 3900
groupolder -1.31 0.53 -2.33 -0.27 1.00 1516 2809
delayhour:groupolder 0.15 0.38 -0.58 0.90 1.00 3260 4860
delayday:groupolder -0.01 0.44 -0.87 0.85 1.00 2423 4031
```

To find if there was an effect between one hour and one day, I ran this code

```
my_samples <- posterior_samples(mod30items)
c <- my_samples$b_Intercept + my_samples$b_delayday
d <- my_samples$b_Intercept + my_samples$b_groupolder
younghour.youngday <- c - d
meanhd <- round(mean(younghour.youngday),2)
cihd <- round(quantile(younghour.youngday, probs = c(0.025, 0.975)),2)
```

This is the result:

```
> meanhd
[1] -0.89
> cihd
2.5% 97.5%
-1.77 0.03
```

As you can see, there is no evidence of an effect of time between one hour and one day. But now look at the graph from conditional_effects. ItÂ´s super obvious that thereâ€™s a difference.

Even more, this is the graph of the probabilities of getting each response right, and itâ€™s the same!

What did I do wrong?

If possible, add also code to simulate data or attach a (subset of) the dataset you work with.

Please also provide the following information in addition to your question:

- Operating System: Windows 10
- brms Version: 2.12.0