Report for: t-tests, non-parametric tests, and large studies—a paradox of statistical practice?

You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.

Title	t-tests, non-parametric tests, and large studies—a paradox of statistical practice?
Published in	BMC Medical Research Methodology, June 2012
DOI	10.1186/1471-2288-12-78
Pubmed ID	22697476
Authors	Morten W Fagerland
Abstract	During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences.

View on publisher site Alert me about new mentions

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Geographical breakdown

Country	Count	As %
United States	2	22%
Hong Kong	1	11%
United Kingdom	1	11%
Norway	1	11%
Unknown	4	44%

Demographic breakdown

Type	Count	As %
Scientists	5	56%
Members of the public	3	33%
Science communicators (journalists, bloggers, editors)	1	11%

Mendeley readers

The data shown below were compiled from readership statistics for 317 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country	Count	As %
Norway	2	<1%
United Kingdom	2	<1%
Netherlands	1	<1%
Czechia	1	<1%
Canada	1	<1%
Russia	1	<1%
United States	1	<1%
Unknown	308	97%

Demographic breakdown

Readers by professional status	Count	As %
Student > Ph. D. Student	55	17%
Student > Master	55	17%
Researcher	42	13%
Student > Bachelor	36	11%
Student > Doctoral Student	17	5%
Other	50	16%
Unknown	62	20%

Readers by discipline	Count	As %
Medicine and Dentistry	47	15%
Agricultural and Biological Sciences	24	8%
Psychology	22	7%
Biochemistry, Genetics and Molecular Biology	20	6%
Computer Science	16	5%
Other	103	32%
Unknown	85	27%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2022.

All research outputs

#2,589,668

of 22,901,818 outputs

Outputs from BMC Medical Research Methodology

#397

of 2,025 outputs

Outputs of similar age

#17,466

of 167,632 outputs

Outputs of similar age from BMC Medical Research Methodology

of 27 outputs

Altmetric has tracked 22,901,818 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.

So far Altmetric has tracked 2,025 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has done well, scoring higher than 80% of its peers.

Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 167,632 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.

We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.

t-tests, non-parametric tests, and large studies—a paradox of statistical practice?

About this Attention Score

Mentioned by

Citations

Readers on

Twitter Demographics

Geographical breakdown

Demographic breakdown

Mendeley readers

Geographical breakdown

Demographic breakdown

Attention Score in Context