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The study described is an example of inferential statistics because the goal is to analyze data from a sample group (probation or parole participants) and use that data to make conclusions about the effectiveness of the rehabilitation program across a larger population.

Before conducting the study, it's important for researchers to create a null hypothesis (H). This would state that the rehabilitation program has no effect on reducing recidivism. Creating a null hypothesis helps define a baseline to test against, using data to either reject or fail to reject the assumption.

An alternative hypothesis (H) should also be created. This would suggest that the program does, in fact, reduce recidivism. Including both hypotheses gives structure to the research and allows for clear comparison during statistical testing.

To evaluate the effectiveness of the program, a chi-square test may be appropriate if researchers are comparing categorical data, like "reoffended" vs. "did not reoffend." If they're comparing mean recidivism rates between two or more groups (e.g., program participants vs. non-participants), an ANOVA or t test might be better. ANOVA is especially helpful if comparing multiple groups. On the other hand, descriptive statistics alone would not be suitable for measuring effectiveness, since they only summarize data and don't allow for conclusions about a larger population.