# Logistic regression shows a significant predictor, but a simpler model makes the same prediction

I am currently puzzled by the classification table SPSS produces for logistic regressions (procedure LOGISTIC REGRESSION). I used the block function for that procedure to produce one model with a single predictor and a second model with two variables. When I compare the two models using the classification tables, both models classify the same number of samples correctly. All numbers in the classification table match exactely. However, at the same time in the more complex model, both predictors have a beta-value with p<0.001. How can this be, if the second factor does not contribute to the overall classification accuracy? In another regression, classification accuracy even goes down for the more complex model, still all predictors have a beta-value with p<0.001.

What exactely is the link between the classification accuracy and the significance values here?