Bootstrap your way into robust inference. Wow, that was fun to write..

**Introduction**

Say you made a simple regression, now you have your . You wish to know if it is significantly different from (say) zero. In general, people look at the statistic or p.value reported by their software of choice, (heRe). Thing is, this p.value calculation relies on the distribution of your dependent variable. Your software assumes normal distribution if not told differently, how so? for example, the (95%) confidence interval is , the 1.96 comes from the normal distribution.

It is advisable not to do that, the beauty in bootstrapping* is that it is distribution untroubled, it’s valid for dependent which is Gaussian, Cauchy, or whatever. You can *defend* yourself against misspecification, and\or use the tool for inference when the underlying distribution is unknown.

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