Statistics, like all mathematical disciplines, does not infer valid conclusions from nothing. Inferring interesting conclusions about real statistical populations almost always requires some background assumptions. Those assumptions must be made carefully, because incorrect assumptions can generate wildly inaccurate conclusions.
Here are some examples of statistical assumptions.
Independence of observations from each other (this assumption is an especially common error).
Independence of observational error from potential confounding effects.
Exact or approximate normality of observations (or errors).
Linearity of graded responses to quantitative stimuli, e.g. in linear regression.