Abstract
This chapter summarizes the basic steps involved in performing commonly used statistical hypothesis tests for continuous and categorical outcomes in randomized clinical trials and further describes the statistical test procedures. Statistical hypothesis tests generally fall into two broad categories, parametric and non-parametric statistical tests. Parametric tests are statistical tests that require the assumption that the data follows some known distribution. These include t-tests, analysis of variance (ANOVA), and χ2 tests. Non-parametric tests do not require this assumption, and are robust to misspecification, but are generally more conservative. Commonly used non-parametric tests include the signed-rank test, Wilcoxon rank sum test, Kruskal-Wallis test, and Fisher’s exact test. While not an exhaustive list, this chapter will describe each of these tests in detail and give an overview of their use.
This is a preview of subscription content, log in via an institution to check access.
References
Agresti A (2011) Exact inference for categorical data: recent advances and continuing controversies. Stat Med 20:17–18
Altman DG, Bland JM (1996) Statistics notes: comparing several groups using analysis of variance. BMJ 312:1472. https://doi.org/10.1136/bmj.312.7044.1472
Fagerland MW (2012) T-tests, non-parametric tests, and large studies – a paradox of statistical practice? BMC Med Res Meth 12:78. https://doi.org/10.1186/1471-2288-12-78
Fisher RA (1922) On the interpretation of χ2 from contingency tables, and the calculation of P. J Royal Stat Soc 85(1):87–94. https://doi.org/10.2307/2340521
Gosho M, Sato Y, Nagashima K, Takahashi S (2017) Trends in study design and statistical methods employed in a leading general medicine journal. J Clin Pharm Therapeutics. https://doi.org/10.1111/jcpt.12605
Guyatt G, Jaeschke R, Heddle N, Cook D, Shannon H, Walter S (1995) Basic statistics for clinicians: 1. Hypothesis testing. Can Med Assoc J 152(1):27–32
Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47(260):583–621. https://doi.org/10.1080/01621459.1952.10483441
Mantel N (1963) Chi-square tests with one degree of freedom; extensions of the mantel-Haenszel procedure. J Am Stat Assoc 58:690–700. https://doi.org/10.1080/01621459.1963.10500879
Pearson K (1900) On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos Mag 50(302):157–175. https://doi.org/10.1080/14786440009463897
Routledge RD (1994) Practicing safe statistics with the mid-p. Can J Stat 22(1):103
Sato Y, Gosho M, Sato Y, Nagashima K, Takahashi S, Ware JH, Laird NM (2017) Statistical methods in the journal – an update. N England J Med 376(11):1086–1087. https://doi.org/10.1056/NEJMc1616211
Yates F (1984) Contingency tables involving small numbers and the chi-square test. J Royal Stat Soc 1:217–235
Zabell SL (2008) On Student’s 1908 article “the probable error of a mean”. J Am Stat Assoc 103(481):1–7
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this entry
Cite this entry
Pond, G.R., Caetano, SJ. (2020). Essential Statistical Tests. In: Piantadosi, S., Meinert, C. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52677-5_118-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-52677-5_118-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52677-5
Online ISBN: 978-3-319-52677-5
eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering