Open Access
Volume 5, 2022
Article Number 9
Number of page(s) 7
Section Life Sciences - Medicine
Published online 13 May 2022

© R.-D. Gosselin et al., Published by EDP Sciences, 2022

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Transparency, correctness, and quality in published protocols are key prerequisites to ensure replication in science and to enable evidence-based research practice. In particular, the use of inappropriate statistical designs and methods as well as their insufficient reporting are considered important contributors to the so-called reproducibility crisis in life science [1]. Guidelines describing statistical reporting are regularly released, thereby establishing an important framework of standardization for published material. These include Animal Research: Reporting of In Vivo Experiments (ARRIVE, recently updated as ARRIVE 2.0) [2, 3], the Checklist for Reporting In-vitro Studies (CRIS) [4], guidelines from the American Physiological Society [5] and the checklist by Emmerich and Harris for in vitro research [6]. Although the aforementioned guidelines are generally addressed to a well-defined scientific community, the current statistical practices in reporting across different research domains remain largely unknown. In this context, meta-research that describes the characteristics of statistical methods in specific research fields would ultimately enable the implementation of targeted educational initiatives in defined research communities.

Immunoblotting (also referred to as “western blotting”) is a very common technique in biochemistry that uses protein separation by molecular weight using Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and a subsequent antibody-based specific detection and relative quantification of proteins of interest. Others have explained that method disclosure in research based on antibodies, including immunoblotting, was not sufficient to enable reproducibility, which prompted the release of specific reporting guidelines in the field [7, 8]. However, these recommendations focused primarily on the disclosure of reagents, laboratory protocols and methods of image acquisition/processing with only minor sections on statistical analysis of quantitative blots.

The present study aims to assess statistical methods used in immunoblotting to rationalize further actions such as comprehensive systematic reviews or the assembling of specific guidelines.

Material and methods

This study was designed and prepared according to the 2018 Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [9] and the 2020 “Updated methodological guidance for the conduct of scoping reviews” [10, 11]. The dual indication was to “examine how research is conducted on a certain topic or field” and serve “as a precursor to a systematic review”. In addition, this manuscript was also prepared in accordance with the ACcess to Transparent Statistics (ACTS) call to action [12]. Therefore, the methods section starts with the statistical paragraph that includes the eight components of ACTS and the discussion incorporates a specific section dedicated to the limitations of the study. For the sake of clarity, throughout the text, the term “sample size” refers to the number of experimental conditions in immunoblots in the articles included in the review, and the term “study size” refers to the number of analytical units in the present review (either the number of articles or total number of experimental conditions included). The study protocol was not preregistered.

Statistical methods

Data were collected, structured, and managed using Microsoft Excel for Mac (version 16.59). Calculations of descriptive statistics (quartiles, interquartile range) and the drawing of bar charts and histograms were performed using the base function in R software (R-studio, version 4.1.1). The statistical items quantified in the study were: the software used, the procedures (tests) implemented, and the number of observations collected (i.e., sample size). The analytical units for the documentation of software and tests were the articles (i.e., the items were listed for each article). For the assessment of sample size, the analytical units were the separate observations (bands), presumed independent, used by the authors in each condition. The study size was determined a priori using a precision-based approach by applying the following formula:

N is the minimum number of articles required to establish a 95% confidence interval (CI, Z = 0.95) for software and tests if the least frequent software or test has a frequency (p) of 0.1 (i.e., present in 10% of articles), with a precision (d) of 0.07 (i.e., 7%). The calculation gave a minimum size of 70 articles for the study.

Article sampling

Articles with quantitative western blots were sampled from the PubMed browser of the NCBI website ( using reverse chronological convenience sampling (Fig. 1, eligibility criteria are presented in Tab. 1). In the advanced NCBI research tool, the following terms were entered for query: “immunoblot” OR “western blot” OR “western-blot” OR “sds page” OR “sds-page” and the NCBI publication filter was used with “end date” set at 31.12.2021. Retrieved articles were individually checked for compliance with inclusion criteria and absence of exclusion criteria by clicking on the link to the journal page or DOI until 70 articles were collected. In particular, the article text and figures were first examined to confirm both that the authors performed an immunoblot (and not a mere polyacrylamide gel electrophoresis) and that at least one reported blot was quantitative (presence of statistics and/or of text providing a quantitative interpretation of the blots). Twenty-six publications were excluded at this stage. During the subsequent detailed analysis of statistical features (see infra), six additional articles were excluded because they did not perform quantitative immunoblots, contrary to what was presumed upon first reading.

thumbnail Figure 1

Flow chart of the sampling protocol.

Table 1

Eligibility criteria applied to the PubMed search engine.

Quantification of statistical features

For each article sampled and for the experiments that reported quantitative immunoblots, the corresponding text (methods, results, discussion, figure legends) was explored, and three statistical items were documented: the statistical software used, the statistical procedure (e.g., test) utilized and the sample size (disclosed number of observations) in each experimental condition. Immunoblots in figures provided as supplemental materials were not included, but supplemental methods referring to figures in the main text were considered full-fledged methodological information. The category “not disclosed/unclear” corresponds to instances where the item was not given, although a statistical analysis (e.g., p-value, discussion on statistical significance) was provided, or was not given in an unambiguous manner. Absence of clear information about a post-test used after ANOVA was included in this latter category.

Data and code availability

All data generated and analysed during this study, as well as the R code used, are publicly available on Figshare at


The final list of included (n = 64) and excluded articles is provided on the Figshare public repository (

Out of the 64 sampled articles, two articles used multiple procedures to analyse their immunoblots. One publication used both Student’s t-test and an unknown post-test after an ANOVA, and no statistical analysis for a blot was interpreted quantitatively. Another article used both an unclear test for one immunoblot figure and no statistical analysis for a western blot interpreted quantitatively. Therefore, the final study size used in the present description of statistical tests used is n = 67. On the other hand, three sampled articles did not provide any statistical analysis or statistical software although they reported quantitative analyses, therefore the final study size for the documentation of software is n = 61 (Fig. 1).

The descriptive analysis of statistical procedures is presented in Figure 2a. The results revealed widespread use of parametric tests, namely one-way analysis of variance (ANOVA) with post-test (15/67), Student’s t-test (regardless of their laterality or paired vs. unpaired nature, 13/67) and two-way ANOVA with post-test (5/67). Non-parametric tests were mostly absent, with only Wilcoxon signed-rank test (1/67), Kruskal–Wallis test (followed by a post-test 1/67), and Duncan’s multiple range test (1/67). Strikingly, in a large proportion of quantitative analyses, the statistical procedure was unclear or not disclosed (23/67, which included instances where the post-test was not disclosed after the omnibus procedure), or no statistical analyses were performed (8/67). The evaluation of statistical software shows that only three packages were cited (Fig. 2b). The most frequently used was GraphPad Prism (36/61), followed by IBM SPSS (10/61) and the open-source software R (1/61). Fourteen articles (14/61) did not cite any software.

thumbnail Figure 2

Quantification of statistical methods (a, study size: n = 67) and software (b, study size: n = 61) in articles (n = 64) with quantitative immunoblotting.

The sample size was clearly indicated in only 1054/2932 experimental conditions and was conversely often unclear (e.g., when inequalities were given instead of exact numbers) or not disclosed (1592/2932). The frequency distribution of sample sizes (Fig. 3) shows the vast over-representation of very small samples, with 744/1054 experimental conditions that incorporated fewer than four observations per sample. In about 10% of experimental conditions (286/2932), the notion of sample size was even irrelevant due to the use of single blots to quantitatively interpret the experiment. The analysis of disclosed sample sizes shows that the median number of observations (i.e., individual blots) used in quantitative immunoblots is 3 (range [2–40], interquartile range, IQR [3–6]).

thumbnail Figure 3

Histogram showing the frequency (Y axis) distributions of sample size (X axis) in immunoblotting experimental conditions (actual study size: n = 1054). Only experimental conditions with clearly disclosed sample sizes were included (1054/2932).


In the sample analysed in the present scoping review, the quantitative immunoblotting was typically designed with small numbers of observations and analysed with parametric tests using proprietary software. The use of such designs jeopardize their validity and sensitivity (i.e., statistical power). First, the verification of parametric assumptions, which are a mandatory prerequisite for the use of parametric tests, is challenging when samples are small. Indeed, in that context, graphical methods such as quantile–quantile (Q–Q) plots are also not reliable and distribution tests for normality (e.g., Shapiro–Wilk, Kolmogorov–Smirnov) tend to be largely underpowered to detect any deviation from normality. Furthermore, statistically significant differences obtained with small samples tend to overestimate effect sizes and to have a higher percentage of false positives among statistically significant results [13]. In addition, small sample sizes such as observed herein tend also to have low statistical power and thus inflate the fraction of false negative results. This shrinkage of statistical power may even engender an impossibility to detect statistically significant differences at the usual confidence levels if non-parametric tests are used [14]. Therefore, for all these reasons, the sampled literature that reports quantitative immunoblotting might be riddled with a large share of statistically unreliable results.

In the present sample, a large proportion of statistical methods were not disclosed, data that is in line with the lack of transparency recurrently reported in other studies [1517], which is a strong impediment to reproducibility. Many guidelines exist but their publication and dissemination has shown limited impact. Therefore, although this route of action appears necessary, especially to provide a framework of official recommendations, it is probably not sufficient to initiate a change towards a more reproducible culture. Strengthening continuing education programmes on good research practices for graduate students, post-doctoral researchers and established researchers would be an important avenue to explore. The organisation of such education for professionals at career stages where improving methodological and statistical literacy relates to the actual daily laboratory activity might be more efficient than targeting young students at the undergraduate level. Furthermore, in methodological courses, a marked emphasis should be placed on experimental design and data sampling rather than mere data analysis and statistical inference [18].

The question of whether research involving immunoblotting uses different standards of design, analytic methods and reporting than other fields of experimental science would be unequivocally answered by more comprehensive systematic reviews. Should it be the case, measures assembled to particularly target this research community might be warranted, including guidelines in field-specific journals, thematic sessions on design and transparency in scientific events or more active education on these topics in biochemistry curricula.

Importantly, the alarmingly high prevalence of non-transparent methods points also to a similar insufficient statistical literacy among peer reviewers in the field. Although guidelines articles intended for peer reviewers are available [7, 19], peer reviewing and editorial filters do not apparently constitute efficient bottlenecks for statistical quality. One solution might be the systematic invitation of statistical reviewers to help ensure standards in statistical reporting while, at the same time, lifting some of the reviewing burden from non-statistical reviewers [20]. Nevertheless, it is conceivable to restrict this option to manuscripts with relatively complex designs, leaving non-statistical reviewers to perform an assessment of statistical transparency and elementary statistics in most studies. In this respect, standardization of minimal statistical knowledge among reviewers is important and would benefit from the widespread creation of continuing education programmes in manuscript peer reviewing [21]. Authoring and reviewing activities are fulfilled by overlapping groups of scholars, and the concepts to be assimilated are the same. Therefore, it is also suggested here that guidelines on reporting practices should systematically be clearly addressed to both researchers and peer reviewers and make explicit that better knowledge of such sound practices is important when engaging in either activity. In addition, specific educational programmes on biostatistics and reporting organized in institutes that perform immunoblotting research should similarly unambiguously clarify that the exposed concepts apply similarly to the design, analysis, and presentation of a research project and its assessment as a reviewer.


The present review has several limitations. First, other statistical features could have been considered to achieve a more comprehensive description of statistical reporting. These include the choice of error, the nature of post-tests, or the significance thresholds used. In addition, this study was designed as descriptive and does not intend to make inferences about all the literature that uses western blots. For example, the date of publication selected and the convenience sampling method may have created an over-representation of some journals and editors that prevents generalization. Nevertheless, the consistency of the results within this sample is already notable, suggesting a widespread entrenched culture of incomplete reporting in immunoblot research and prompts for future confirmatory systematic review. Since not all periodicals that publish immunoblots are referenced in PubMed, the use of this sole database may also be a source of bias. Furthermore, the study protocol was not preregistered, which constitutes another limitation. Finally, the present study was designed and analysed by a single reviewer, a methodology deemed acceptable for accelerated evidence synthesis such as the current review [22, 23] but which might increase error and bias for comprehensive systematic reviews [23, 24]. Therefore, future systematic reviews would ideally involve two reviewers, use a large sample of articles from at least two databases and a sampling scheme that enable the random collection of immunoblot studies on a larger time period (e.g., 6 months) and from many distinct journals.


In conclusion, the results indicate that small samples, parametric tests and proprietary software might be omnipresent in immunoblotting research and that non-transparency in statistical protocols is widespread. Targeting researchers in this specific field with continuing education on good research practice and fundamental biostatistics should be a priority objective. Additional avenues to explore are better communication or enforcement of existing reporting guidelines. Journals and editors should take a leading role in these efforts.

Conflict of interest

The author declares no competing interests. The research was supported (RDG salary as an employee of the Lausanne University Hospital (CHUV)) by institutional intramural funding.


The author would like to thank Prof Jacques Fellay for his support. The author declares no competing interests. The research was supported (RDG salary as an employee of the Lausanne University Hospital (CHUV)) by institutional intramural funding.

Author contributions

RDG designed the study, analysed the data, and wrote the manuscript.


Cite this article as: Gosselin R-D 2022. A snapshot of statistical methods used in experimental immunoblotting: a scoping review. 4open, 5, 9.

All Tables

Table 1

Eligibility criteria applied to the PubMed search engine.

All Figures

thumbnail Figure 1

Flow chart of the sampling protocol.

In the text
thumbnail Figure 2

Quantification of statistical methods (a, study size: n = 67) and software (b, study size: n = 61) in articles (n = 64) with quantitative immunoblotting.

In the text
thumbnail Figure 3

Histogram showing the frequency (Y axis) distributions of sample size (X axis) in immunoblotting experimental conditions (actual study size: n = 1054). Only experimental conditions with clearly disclosed sample sizes were included (1054/2932).

In the text