Better than Casella and Berger's *Statistical Inference*? I have just finished an undergraduate degree in statistics and am looking into a graduate degree in statistics.
One textbook that I've found in my searching is Casella and Berger's Statistical Inference (which is supposed to cover the theory of probability and statistics); however, I've found that many have stated that it lacks clarity.
Is there a "better" reference for this topic?
Note: I used Wackerly et al.'s Mathematical Statistics with Applications when I was an undergraduate.
 A: The best choice might depend on which type of book you would prefer. In my opinion:


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*If you want to privilege clarity, I would suggest you "Models for Probability and Statistical Inference: Theory and Applications" by James H. Stapleton: this is a relatively short but clear and comprehensive book on probability and statistical inference, with a lot of images and simulations that can be very useful;

*If you are interested in completeness, I would suggest you "Probability and Statistical Inference"  by Robert V. Hogg: this is a classical "milestone" book on this topic, a very comprehensive course that in its last edition is enriched by a CD with several data sets formatted for most statistical software packages;

*If you are interested in a "mathematical" approach, an appropriate choice could be "Mathematical Statistics, Basic Ideas and Selected Topics" by Peter J. Bickel: this is a high-level book written from a strictly mathematical  standpoint, containing good sections also on inference and probability theory;

*If you search something midway between simplicity and completeness, maybe a good text could be "Introduction to Probability Theory and Statistical Inference" by Harold J. Larson: despite relatively older, in my opinion this remains a very clear statistical course.
I hope that these advices could be helpful to you.
