It depends. The most important assumption is independence of the observations. If that is not given, you have to either account for correlated responses using a mixed-effects model or mean-aggregate those responses (computing the mean decreases the variance but also reduces the number of data points and those two cancel each other out in calculating the t-statistic of the Wald test).

With regard to other assumptions, e.g. normality of the residuals, linear models can often deal with some degree of violation against those. But I agree that it's always good to understand the influence of those violations, e.g. by using simulations and making p-value histograms of null-data.