I haven't read any of those textbooks but a quick look at their table of contents makes me think you might find the first (Statistical Models by A.C. Davison) most helpful. That one takes the most applied perspective; the other two focus on the rigorous mathematical underpinnings of statistical inference. For doing actual data analysis, you will likely be better off with a textbook that has an applied perspective.
Even the Davison text, though, isn't specific to experimental data analysis. Its section on analysis of variance (a key statistical method in analysis of many experiments) is only eight pages long. The chapter on designed experiments is 47 pages long, which may seem like a lot until you realize that entire textbooks are written on design and analysis of experiments. Once you've covered basic statistical ground as in the Davison textbook (or more rigorously in one of the other books), you might be ready to focus specifically on experiments.
Most textbooks that cover data analysis for experimental data also cover the design of experiments. As you can see from questions like this one -- How to test hypothesis for group differences -- the analysis of experimental data is tied up with the design of the experiment. Poor experimental design can ruin your chances of answering the questions you want to answer. Even if you are not the one doing the experimental design, you need to know what a well-designed experiment looks like. Ideally, you will be involved in the design so that you can ensure that the data you'll have for analysis will be useful.
If you do decide to get a book focused on experimental design and analysis, you'll want to get one that is specific to your domain. I like Myers, Well, and Lorch's Research Design and Statistical Analysis. I've found it useful in a social science research setting. But if you're working in another area (clinical trials or ecology, for example), you can find books that cover experimental design and data analysis in those contexts.