The　skewed　generalized　normal　(SGN)　distribution　with　four　parameters　is　a　versatile　distribution　that　can　effectively　model　data　with　skewness　and　heavy　or　light　tails.　In　this　paper,　we　conduct　two　classes　of　goodness　of　fit　tests　for　the　SGN　distribution　based　on　the　empirical　distribution　function　(edf)　and　the　sample　correlation　coefficient.　The　first　class　involves　transforming　the　sample　into　approximately　mixed　gamma　observations,　and　then　applying　five　classical　parametric　bootstrap　edf-based　goodness　of　fit　tests.　The　second　class　is　based　on　the　inverse　probability　transformation　and　utilizes　the　sample　correlation　coefficient　as　the　test　statistic.　We　compare　the　finite　sample　performances　of　the　proposed　tests　for　different　sample　sizes　and　alternative　distributions　by　extensive　numerical　studies.　The　simulation　results　demonstrate　that　the　proposed　tests　provide　a　valid　alternative　to　the　standard　tests　using　the　original　data,　and　the　analysis　of　real　data　illustrates　its　application.　©　2024　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.