Different types of Scientific Studies — Strength of Evidence

In today’s Medium article I’ll be writing up about the different types of science papers/studies that are out there and the strength of each one. As there is a hierarchy in the strongest type of evidence for studies. So without further ado let’s get into the article.

Source: https://www.researchgate.net/figure/Hierarchy-of-evidence-pyramid-The-pyramidal-shape-qualitatively-integrates-the-amount-of_fig1_311504831

➡ Types of Studies

Within science, there are many types of paper/studies that form the hierarchy of scientific evidence. The different types of studies are as follows (weakest to strongest) [1]:

● Opinion letters & correspondence studies
● Case reports
● Animal studies
● In vitro [2] (see reference for definition)
● Cross-sectional study
● Case-control study
● Cohort studies
● Randomised controlled trial
● Systematic reviews and meta-analyses [3] (see reference for definition)

Reference: [1] https://thelogicofscience.com/2016/01/12/the-hierarchy-of-evidence-is-the-studys-design-robust/
[2] https://www.healthline.com/health/in-vivo-vs-in-vitro#definitions
[3] https://himmelfarb.gwu.edu/tutorials/studydesign101/metaanalyses.cfm

➡ Study Sample Size

Another important way to determine if a study is reliable would be via the pool of participants used for the meta-analysis as one example. As quoted from the Institute for Work & Health the meaning of sample size is as follows — “Sample size refers to the number of participants or observations included in a study. This number is usually represented by n. The size of a sample influences two statistical properties: 1) the precision of our estimates and 2) the power of the study to draw conclusions.” [1]

It’s really important to have a good robust number are participants for a study to draw concrete conclusions from and have great knowledge to interpret the data. The lower the sample size, the lower the robust evidence the study has. As Science data can be refuted with better data.

Sample sizes can influence research outcomes for any study [2], regardless of how strong the type of study it can be, as the above section is only a mere basic guide. Very small samples undermine the internal and external validity of a study, even having a very large sample size has it disadvantages but not so much when compared to a study of a few hundred participants.

References: [1] https://www.iwh.on.ca/what-researchers-mean-by/sample-size-and-power
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4296634/

➡ Hierarchy of Evidence

The evidence-based scientific pyramid definitely has a hierarchy to evaluate healthcare interventions [1] as one example. Each and every study type has its own strengths and weaknesses, but more so at the bottom of the scientific evidence pyramid than anything else. The hierarchy is there to ensure the strength of precision of research methods [2] (more explanation at the reference), this answers you have the robust best evidence at hand and can draw much more informed conclusion from.

Reference: [1] https://onlinelibrary.wiley.com/doi/full/10.1046/j.1365-2702.2003.00662.x
[2] https://canberra.libguides.com/c.php?g=599346&p=4149721

➡ Meta-Analysis

The top of the food chain for any study (if reliable) is a meta-analysis study [1], a meta-analysis study is where results from numerous to lots other studies are combined to produce an overall stat.

The golden standard of meta-analysis and systematic reviews are conducted by the Cochrane Library [2], Cochrane own meta-analysis study of Ivermectin for example [3] came back and stated that the overall evidence doesn’t support the use of IVM for treatment or prevention of Covid-19 outside well designed RCTs [4], so this is one major example where the strongest piece of scientific evidence gives individuals very robust and informed conclusions on one subject unless better data comes out to say otherwise (which it probably won’t).

References: [1] https://uk.cochrane.org/news/meta-analysis-what-why-and-how
[2] https://www.cochranelibrary.com/
[3] https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD015017.pub2/full
[4] https://emj.bmj.com/content/20/2/164

➡ Conclusion

It’s all well and true just looking at studies, but we must be able to understand the type, how many participants there are used in the studies (like RCTs as one example) thus determining whether or not there are better data out there that informs us to get a better pool of knowledge on a subject. The more robust your counter-evidence to someone in a debate, the stronger your points come across, especially in the wake of meta-analysis studies.

💥 Thanks for reading, Lawrence. Please consider a small contribution, in the form of a beer as all articles are created in my small amount of spare time: https://www.buymeacoffee.com/LawrenceRob



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Lawrence Robinson

Passionate about evidence-based scientific information and tackling falsehoods that thrive on social media.