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Hypothesis testing assesses whether data are inconsistent with a given hypothesis (usually a null hypothesis of no effect).
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What test to compare two dichotomous values?
Depending on the magnitude of the count data, linear regression might be the way to go.
If the counts are relatively small, a poisson regression might be the way to go.
I'd be careful about interpre …
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Test for two proportions
Since you have a mulitnomial outcome, the groups are not independent so I don't think a binomial test ignoring the third category would be best.
The data are multinomial with 3 categories. This means …
1
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Accepted
Why Power = P(True Positive) in Hypothesis testing?
If I were to describe it using the language of classification, statistical power concerns the probability we find a true positive (the effect truly is not the null effect, and we have correctly detect …
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Choosing statistical test
I don't think you need a logistic regression. The outcome is binary (Left/Right is Most Persuasive) if I have understood correctly. The hypothesis is about the order in which arguments are presented …
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Is the issue of multiple testing related to doing several tests on the same sample?
Technically, the probability of making at least one false positive will increase assuming the null is true. However, there is typically no need to correct for this as the two measures can be considere …
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Significance and certainty in hypothesis testing
Wow, really good question. Let me see if I can add something.
My question is: If results start to pile up in one rejection region after a lot of runs, how long will we believe in the plausibility …
1
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Accepted
Why is the type 1 error rate equal to the alpha level, and not the P-Value?
So I guess my question is why would the chance of a false positive/type 1 error be 5% in the 1st example and not 3%?
By construction. It may help to think a little bit about what is happening in Nu …
2
votes
How to do you find a power of a test of two means?
This can actually be framed as a linear regression, so let's use a power formula found in this book.
The power to detect an effect of $\beta$ is
$$ \gamma = 1 - \Phi(z_{1-\alpha/2} - \vert \beta \ve …
2
votes
Accepted
Hypothesises testing
The null and alternative would be $H_0: p = 0.5$ and $H_A: p \neq 0.5$. Here, $p$ is the proportion of the population which are happy.
The standard deviation is a function of the sample proportion …
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Accepted
How to test the impact of several variables on the order rate of a given product?
The term you will want to search for is "AB Testing". In short, AB testing creates two variants of your magazine (version A and B, hence the name) and randomly distributes them to salesmen and custom …
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If p=0.999 why can't I conclude that there is zero or near-zero effect?
I can conclude that there is no difference between groups
Not quite. A more precise conclusion would be that the data are consistent with there being no difference. However, we can't know for sure …
3
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Accepted
Hypothesis testing: Difference of means (pre-treatment)
Your conclusion is/was correct. Randomization is not about balancing groups. Indeed, there is a good chance that unobserved confounders are unbalanced and yet randomization still works. In fact, t …
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Probability of failing to reject null?
whuber's answer is good, but I think I can simplify things a little bit.
When $\mu_A = \mu_b$, the probability we reject $H0$ is simply the type one error $\alpha$. This is true because $H0$ is inde …
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1
answer
329
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Can a wald test be computed for Binomial data with 0 successes?
Suppose I observe 0 successes out of $n$ binomial trials.
A Wald test would have me compute
$$ \dfrac{(\hat{\pi} - \pi_0)^2}{\widehat{V}(\hat{\pi}_j)} \sim X^2_1$$
But the estimated variance is 0 …
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1
answer
526
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A test for missing at random
I have data about locations (Provinces, cities, postal codes, etc). Some of my postal codes are missing, and I think there may be a relationship between province and the missing postal code.
I can d …