Testable hypotheses construction; minimum predictive strength vs. significance Is this null hypothesis TESTABLE?  


Research Question: "Can a predictive model utilizing logistic regression be built which predicts at least one customer will churn in 90 days, and this individual prediction will be at a minimum of 70% confidence, using a chosen set of independent variables?"  
Null Hypothesis: "A predictive model utilizing logistic regression cannot  predicts at least one customer will churn in 90 days, with this individual prediction being at a minimum of 70% confidence, using a chosen set of independent variables."  


If so, how do you test the "70% confidence" of the particular churn event that allows you to reject the null hypothesis, at the p &lt; 0.05 level?  I'm not sure the confidence of an individual prediction can be done, but that may depend on the modeling technique.  It may be that you drop the p &lt; 0.05, and just use the validity of the full model (R-square and Pearson chi-square with logistic regression). 

Or, is this the only way to write the null hypothesis, and the strength of the model must be left to the discussion section? 


"There is no significant relationship between the full model using the chosen independent variables and customer churn."  (With r-square and chi-square showing the significance of the full model.)
Good question.  By convention, the null hypothesis is always framed to say there is no statistical relationship between one or more independent variables and a dependent variable.  This is typically tested at the p &lt; 0.05 level.  The level of the relationship beyond it being significant is not addressed in a null hypotheses.  (See, for example, Encyclopedia of Research Design, edited by N.J. Salkind, 2010).  As for testing the "70% confidence" result, there is no way to do that.  That is the statistical result.  The overall validity of a logistic regression model can be assessed using Pearson's chi-squared, R-square, and classification accuracy.