When you require the most reliable model be created to predict an outcome or map a sample to a population, simply running the model once on the sample data on hand may not be the best approach because results are dependent on your sample data. Resampling with replacement will provide you with more accurate estimates of the reliability of your data.
To identify precisely how suitable your model is, you will want to bootstrap the model to assess its stability
Through resampling, IBM SPSS Bootstrapping software can create thousands of alternate versions of your data set, providing a more accurate view of what is likely to exist in the population. (Its default setting is 1,000 samples, but this setting can be modified upward or downward.)
Through the IBM SPSS Bootstrapping dialog box, you can more easily control the numbers of bootstrap samples, set a random number seed, specify confidence intervals, and indicate whether a simple or stratified method is appropriate.