An outcome-dependent sample is generated by a stratified survey design where the stratification depends on the outcome. It is also known as a case–control sample in epidemiological studies and a choice-based sample in econometrical studies. An outcome-dependent enriched sample (ODE) results from combining an outcome-dependent sample with an independently collected random sample. Consider the situation where the conditional probability of a categorical outcome given its covariates follows an explicit model with an unknown parameter whereas the marginal probability of the outcome and its covariates are left unspecified. Profile-likelihood (PL) and weighted-likelihood (WL) methods have been employed to estimate the model parameter from an ODE sample. This article develops the PL- and WL-based families of tests on the model parameter from an ODE sample. Asymptotic properties of their test statistics are derived. The PL likelihood-ratio, Wald and score tests are shown to obey classical inference, i.e. their test statistics are asymptotically equivalent and Chi-squared distributed. In contrast, the WL likelihood-ratio statistic asymptotically has a weighted Chi-squared distribution and is not equivalent to the WL Wald and score statistics. Our theoretical derivation and simulation show that tests based on these new statistics carry nominal type I error and good power. Advantages of ODE sampling together with the implementation of the PL and WL methods are demonstrated in an illustrative example.