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I am looking for a book that is focused on hypothesis testing. I read "Hypothesis Testing: An Intuitive Guide for Making Data Driven" by Jim Frost and I'm looking for similar book which is focused on practical applications of diferent tests (and exaplains how they works and when to use it)

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  • $\begingroup$ Are you looking at a generic hypothesis testing book, or for something with some quantitative finance nexus? $\endgroup$ Aug 16 at 16:33
  • $\begingroup$ it would be ideal if the book would mainly focus on these tests used in the finance and cover them in great detail $\endgroup$
    – Hugo
    Aug 16 at 16:39
  • $\begingroup$ Great, and are you comfortable with the amount of math in this one? $\endgroup$ Aug 16 at 16:47
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    $\begingroup$ @Hugo: The book that Dimitri recommended is the absolute classic-bible on hypothesis testing but it leans towards the theoretical side. ( Lehman is one of the top stat guys in the 20th century ). It's been ages since I looked at it but I'm 99.99 percent sure that there won't be one finance application it. I looked around by googling and unfortunately I couldn't find anything with finance examples. Dimitri can correct me if I'm wrong because it's been 20 years atleast since I used it. $\endgroup$
    – mark leeds
    Aug 16 at 22:38
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    $\begingroup$ @DimitriVulis I'm a charter holder so it's free for me to look. From a quick glance it doesn't seem worthwhile if you're already experienced with hypotheses testing. There is only 42 pages of text starting with an explanation what hypotheses testing is. I looked at the references as well, from a first look they concern other general statistics texts or papers about specific events. $\endgroup$
    – Bob Jansen
    Aug 17 at 20:17
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I dug up this book: Svetlozar Rachev, Markus Höchstötter, Frank Fabozzi, Sergio Focardi. Probability and Statistics for Finance. Wiley (2011). Chapter 19 is "Hypothesis Testing". It does not go as deep into the theory as Lehman. Starting on page 496, it lists several concrete and practical examples pertinent to finance and markets, worked out in lots of detail.

1 (simple test for parameter $\lambda$ of Poisson distribution) Consider a portfolio of risky bonds, where the number of defaulting bonds within one year is modeled as a Poisson random variable with parameter $\lambda$. The null hypothesis $H_0$ is that $\lambda=\lambda_0$, while the alternative hypothesis $H_1$ is that $\lambda=\lambda_1$, for some values $\lambda_0$ and $\lambda_1$.

2 (1-tailed test for parameter $\lambda$ of exponential distribution) Same risky bond portfolio, and the time between two successive defaults is given by an exponential random variable with parameter $\lambda$. The null hypothesis $H_0$ is that $\lambda \ge 1$, e.g. 1, while the alternative hypothesis $H_1$ is that $0 < \lambda <1$.

3 (1-tailed test for the mean $\mu$ of a normal distribution where the variance is known.) $\mu$ is the mean of the daily returns of a stock index. The null hypothesis $H_0$ is that $\mu \le y$, while the alternative hypothesis $H_1$ is that $\mu > y$ for some value $y$.

4 (1-tailed test for the variance of a normal distribution where the mean is known.) Same stock index, the null hypothesis $H_0$ is that $\sigma^2 >v$, while the alternative hypothesis $H_1$ is that $0 < \sigma^2 <v$ for some value $v$.

5 (2-tailed test for the mean $\mu$ of a normal distribution where the variance is known.) Same stock index, the null hypothesis $H_0$ is that $\mu = y$, while the alternative hypothesis $H_1$ is that $\mu \ne y$ for some value $y$.

6 (Equal tails test for the variance of a normal distribution where the mean is known.) Same stock index, the null hypothesis $H_0$ is that $\sigma^2 = v$, while the alternative hypothesis $H_1$ is that $\sigma^2 \ne v$ for some value $v$.

7 Test for equality of means: given two stocks, are their mean returns the same...

8 2-tailed Kolmogorov-Smirnov test for equality of distribution...

9 Likelihood ratio test...

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