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- Hypothesis testing: Fishers exact test and Binomial test
The result obtained with the Fisher's exact test ("no significant difference between the proportion of girls and boys who finds that the cake tastes good") seems to contradict the results in (1) and (2), which say that the "more than 50% of the population of girls find that the cake tastes good" (1), and "no more than 50% of boys find that the
- How to resolve the ambiguity in the Boy or Girl paradox?
The net effect is that even if I don't know which one is definitely a boy, the other child can only be a girl or a boy and that is always and only a 1 2 probability (ignoring any biological weighting that girls may represent 51% of births or whatever the reality is)
- probability - What is the expected number of children until having the . . .
You can consider starting from position 1 for the difference of boys girls and move up and down randomly with 50% probability until reaching zero These type of walks have been described here: What is the distribution of time's to ruin in the gambler's ruin problem (random walk)? and based on the results in those answers we can see that the
- what is the difference between a two-sample t-test and a paired t-test
While I was glancing at hypothesis tests, I saw paired and two-sample t-test but couldn't understand the difference For the explanation of these two tests, I saw the following sentence quot; Two-
- r - What test can I use to compare slopes from two or more regression . . .
Seating 5 boys and 5 girls in a row when no two girls may sit together VS forming a line with the five boys and five girls alternating What is difference between using angle and arc length as oscillating variables of a mathematical pendulum?
- should I use log or raw data in non parametric tests?
If you only compare girls vs boys, Kruskall-Wallis test and Wilcoxon test should be equivalent But anyway, as for the Wilcoxon test, the Kruskall-Wallis test is not affected by log-transform, as explained by stats_model $\endgroup$ –
- Whats the difference between variance and standard deviation?
The standard deviation is the square root of the variance The standard deviation is expressed in the same units as the mean is, whereas the variance is expressed in squared units, but for looking at a distribution, you can use either just so long as you are clear about what you are using
- Why is gender typically coded 0 1 rather than 1 2, for example?
$\begingroup$ Using a 0 1 coding scheme is essentially useful when applying regression models among others, although several coding schemes are possible, e g -1 1 (but it will change the interpretation of the regression coefficients)
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