Model testing is a central step of spatial point pattern analysis, which allows ecologists to judge if their data agree with ecological hypotheses. We present a simple and elegant solution of a challenging problem: the construction of a goodness‐of‐fit envelope test with prescribed significance levelα. Our new Analytical Global Envelope (AGE) test is not restricted to the narrow frame of complete spatial randomness testing and its envelopes can be determined by mathematical calculations. This allows us to investigate the influence of key settings of theAGEtest on the width of the envelope strip. To circumvent some assumptions of the simulation‐freeAGEtest we present a corresponding Simulation‐Based Global Envelope (SBGE) test. The envelope strip of theAGEand theSBGEtest encircles the range of a summary function such as the pair correlation function under the null model, and it has the desired property that the null hypothesis can be rejected with significance levelαif the empirical summary function wanders outside the envelopes. TheAGEtest can be applied under the mild conditions that the values of the summary functions under the null model are (approximately) normally distributed and are (approximately) independent for different distance binsr_j. TheSBGEtest requires only the independence assumption. The width of the strip of theAGEenvelopes scales for a broad range of point processes with 1/n, wherenis the number of points. This casts doubt about attempts of goodness‐of‐fit testing with lown(say <100). TheAGEandSBGEtest operate with wider envelope strips than the classical “pointwise” test. Therefore, the pointwise test has to be considered as too liberal. Furthermore, we show that the width of theAGE/SBGEstrip increases approximately with ln(b), wherebis the number of distance bins. For example, theAGE/SBGEenvelopes are forb = 20 more than 50% wider than the corresponding pointwise envelopes. Our study opens up new avenues to the test problem in point pattern statistics and the newAGEandSBGEtests can be widely applied in ecology to improve the practice in null model testing.