TAILIEUCHUNG - Lập Trình C# all Chap "NUMERICAL RECIPES IN C" part 127

Tham khảo tài liệu 'lập trình c# all chap "numerical recipes in c" part 127', công nghệ thông tin phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Chapter 14. Statistical Description of Data Introduction In this chapter and the next the concept of data enters the discussion more prominently than before. Data consist of numbers of course. But these numbers are fed into the computer not produced by it. These are numbers to be treated with considerable respect neither to be tampered with nor subjected to a numerical process whose character you do not completely understand. You are well advised to acquire a reverence for data that is rather different from the sporty attitude that is sometimes allowable or even commendable in other numerical tasks. The analysis of data inevitably involves some trafficking with the field of statistics that gray area which is not quite a branch of mathematics and just as surely not quite a branch of science. In the following sections you will repeatedly encounter the following paradigm apply some formula to the data to compute a statistic compute where the value of that statistic falls in a probability distribution that is computed on the basis of some null hypothesis if it falls in a very unlikely spot way out on a tail of the distribution conclude that the null hypothesis is false for your data set If a statistic falls in a reasonable part of the distribution you must not make the mistake of concluding that the null hypothesis is verified or proved. That is the curse of statistics that it can never prove things only disprove them At best you can substantiate a hypothesis by ruling out statistically a whole long list of competing hypotheses every one that has ever been proposed. After a while your adversaries and competitors will give up trying to think of alternative hypotheses or else they will grow old and die and then your hypothesis will become accepted. Sounds crazy we know but that s how science works In this book we make a somewhat arbitrary distinction between data analysis procedures that are model-independent and those that are model-dependent. In the former .

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