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4 votes
Accepted

Why is my pvalue for chisquared test so high?

Whether you meant to do this or not, your null hypothesis is that there is a 3:1 ratio. Indeed, there is such a 3:1 ratio at the population level, and your empirical data show roughly a 3:1 ratio. ...
Dave's user avatar
  • 65k
4 votes

How to show that many functions (a hundred, a thousand) have the same shape an distribution of values over an interval?

To show that several functions are more or less the same you could just superimpose them graphically. I don't think quantile plots of any flavour are directly relevant or likely to be helpful. The ...
Nick Cox's user avatar
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4 votes

How does one develop an intuitive understanding of statistical methods and the ability to "know" which test to use, why, and how to interpret results

I teach statistics to biology students. They're usually less than thrilled about statistics coming in. They also come to us for statistical consultancy during research projects Personally, the biggest ...
3 votes

Why is my pvalue for chisquared test so high?

It's not clear where your doubt arises. The standard error of your proportion (either one) under $H_0$ is $\sqrt{\frac{1\times 3}{4\times 4 \times 100000}}\approx 0.00137$. Meanwhile the "error&...
Glen_b's user avatar
  • 286k
2 votes

Can hypothesis testing be done on two points?

I see it as a test on equality of proportions. This, however, only would hold if you had the exact numbers. You can take a look here for performing such test in R: http://www.sthda.com/english/wiki/...
Federico Tedeschi's user avatar
2 votes
Accepted

ANOVA finding a main effect prevents inflation of Type I error rate

...these multiple comparisons do not inflate the Type I error rate because they are only conducted if the ANOVA finds a main effect. This is an example of right for the wrong reasons. Even if the ...
Frans Rodenburg's user avatar
1 vote

Statistical Inference on Samples vs Populations

You write "I want to know if this change (2%) can be attributable to randomness or non-randomness (i.e. statistically significant)." But "statistical significance" is not merely a ...
Graham Wright's user avatar
1 vote

Statistical Inference on Samples vs Populations

I already wrote in comments that chances are applying any standard test to the example situation would be misleading/invalid even in case that the "2nd interpretation" is taken. However in ...
Christian Hennig's user avatar
1 vote

The F-test and the t-test reject the null hypothesis while the KS test and the Chi-Squared test do not

The lack of rejection by those tests is not a confirmation of their null hypotheses. What the lack of rejection means is that there is insufficient evidence for that particular test to refute the null ...
Dave's user avatar
  • 65k
1 vote

Non-parametric one-sample mean test for a bounded variable (based on Chebyshev's inequality?)

Let $\mathcal B$ be the ensemble of distributions of random variables bounded to $[0, 1]$. We have $n$ variables $x_1 ... x_n$ IID from some distribution in $\mathcal B$. We want to test the null $E(X)...
Guillaume Dehaene's user avatar
1 vote

Underpowered studies and minimum effect size

Wouldn't we always want to specify (our best estimate of) the true effect size? YES! Always specify the observed effect and if your best estimate differs from that then you must say how and why. And ...
Michael Lew's user avatar
  • 16.2k
1 vote

Underpowered studies and minimum effect size

It depends on the context. For one, if you have good reasons to think the true effect size is much smaller than the smallest effect size of interest (SESOI), and if that means that the SESOI is ...
J-J-J's user avatar
  • 4,944
1 vote

What criterion to use to compare multiple correlations of binary variables?

I think the problem might be that your formulation of null-hypothesis is not actionable. 'Question formulated less clearly than the rest' ... how would one derive something computable from it? You ...
Cryo's user avatar
  • 783

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