Pearson Residual vs Hosmer-Lemeshow Goodness of Fit (GOF) Test

Dear all,

I am currently building a predictive model and would like to test its calibration (i.e. model fit). I came across two approaches: Pearson residual vs HL test. Given that my sample size is 50 (~40 diseases vs 20 healthy), which approach should I use? I tried two approaches but they yielded completely different outcomes (the one by Pearson gives me "good fit" but the HL test suggests otherwise). I have two predictors, both continuous variables (Age & BMI).

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