The probability that you will incorrectly fail to reject the null hypothesis is called "beta".
1. Are there any other predictors of divorce? If so, how good are they? If this is the only reliable effect, that's noteworthy even if it's not very large. Or perhaps there are other predictors, but meeting online is the factor that explains the most variance, or is at least in the top two or three. I mean, I kind of doubt it, but stranger things have been observed.
2. What is the application case for this information, and how practical is it to collect? Maybe you're a lawyer who wants to improve ad targeting and it turns out to be very easy to predict whether a particular viewer met their partner online: in that case, you only need to do a very little better than random in order to improve your targeting, because you're planning to get the ad shown to millions of people. However, if it's hard or expensive to tell whether they met their partner online, or if there is a big penalty to guessing wrong, then suddenly even at the same effect size it's not worth it anymore.
model <- overfit(sexy.variable ~ weather * stock.prices * whatever.the.fuck + 1)
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