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1 vote

Sample size calculation for Cox regression with time-varying exposures

There are no closed form solutions for such a power calculation. I would suggest estimating power via simulation.
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1 vote

How to correct for small p-value due to very large sample size

A few years later... The paper by Naaman (2016) Almost sure hypothesis testing and a resolution of the Jeffreys-Lindley paradox seems relevant. From the abstract: A new method of hypothesis testing ...
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  • 3,110
0 votes

Sample size for rare diseases

Adaptive test If the treatment allows this then you might develop a test in which you increase the treatment group during time. How fast you do this depends on the recovery rate. In the simplest case ...
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1 vote

How to calculate number of participants required to compare mean scores on a questionnaire between two groups?

It is a pretty old question here but since I know of, and made use of a straightforward way of calculating this I ‘d like to share it depending on whether you wish to have a double sided or a single ...
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  • 11
0 votes

determining the sample size for quality test

I originally thought this could be done with a binomial approximation, but I've since changed my mind. Because you have a finite sample (N=200 documents) then we can do Bayesian inference to determine ...
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2 votes
Accepted

Comparing percentages from different sample sizes

You need to take into account both the different numbers of cells from each animal and the likely correlations of responses among replicates/cells taken from each animal. You have more confidence in ...
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-1 votes

Why does Machine Learning need a lot of data while one can do statistical inference with a small set of data?

Machine Learning and Statistical inference deal with different type of problems and are not comparable in this point of view. Statistical inference is used in problems that are inherently statistic, ...
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4 votes

Why does Machine Learning need a lot of data while one can do statistical inference with a small set of data?

Machine learning (often) needs a lot of data because it doesn't start with a well defined model and uses (additional) data to define or improve the model. As a consequence there are often a lot of ...
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18 votes

Why does Machine Learning need a lot of data while one can do statistical inference with a small set of data?

Machine learning does not require large amounts of data, it is just that the current bandwagon is for models that work on big data (mainly deep neural networks, which have been around since the 1990s, ...
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3 votes

Why does Machine Learning need a lot of data while one can do statistical inference with a small set of data?

A typical machine learning model contains thousands to millions of parameters, while statistical modelling is typically limited to a handful parameters. As a rule of thumb, the minimum an amount of ...
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31 votes

Why does Machine Learning need a lot of data while one can do statistical inference with a small set of data?

All/other things being equal (when?) machine learning models require similar quantities of data as statistical models. In general statistical models tend to have more assumptions than machine learning ...
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