| 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 | |
| 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. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 9% |
| Hong Kong | 1 | 9% |
| United States | 1 | 9% |
| United Kingdom | 1 | 9% |
| Norway | 1 | 9% |
| Unknown | 6 | 55% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 45% |
| Members of the public | 5 | 45% |
| Science communicators (journalists, bloggers, editors) | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 392 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 | 383 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 63 | 16% |
| Student > Ph. D. Student | 62 | 16% |
| Researcher | 52 | 13% |
| Student > Bachelor | 39 | 10% |
| Student > Doctoral Student | 20 | 5% |
| Other | 55 | 14% |
| Unknown | 101 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 47 | 12% |
| Psychology | 27 | 7% |
| Agricultural and Biological Sciences | 26 | 7% |
| Biochemistry, Genetics and Molecular Biology | 25 | 6% |
| Computer Science | 21 | 5% |
| Other | 121 | 31% |
| Unknown | 125 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 06 January 2024.
All research outputs
#2,706,775
of 25,837,817 outputs
Outputs from BMC Medical Research Methodology
#402
of 2,318 outputs
Outputs of similar age
#16,794
of 182,898 outputs
Outputs of similar age from BMC Medical Research Methodology
#3
of 28 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,318 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has done well, scoring higher than 82% 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 182,898 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.