Keita has a juice stand. He made a new sign that he thinks will attract more customers than the current sign.
He randomized $60$ workdays between a treatment group and a control group. On each day from the treatment group he put up the new sign, and on each day from the control group he put up the old sign.
The results of the experiment showed that the mean number of bottles sold under the new sign is $12$ more than the mean number of bottles sold under the old sign. To test whether the results could be explained by random chance, Keita created the table below, which summarizes the results of $1000$ re-randomizations of the data (with differences between means rounded to the nearest $2$ bottles).
**According to the simulations, what is the probability of the treatment group's mean being higher than the control group's mean by $12$ bottles or more?**
$\qquad$[[☃ input-number 1]]
Assume that if the probability you found is *lower* than $5\%$, then the result should be considered as significant.
**What should we conclude regarding the experiment's result?**
[[☃ radio 1]]
Treatment group mean $-$ Control group mean | Frequency
:-: | :-:
$-14$ | $7$
$-12$ | $18$
$-10$ | $20$
$-8$ | $46$
$-6$ | $97$
$-4$ | $103$
$-2$ | $123$
$0$ | $152$
$2$ | $138$
$4$ | $107$
$6$ | $92$
$8$ | $57$
$10$ | $23$
$12$ | $13$
$14$ | $4$