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Oct 7, 2024 · This article provides a visual, interpretable guide supported by real-world examples to help you choose the right statistical test depending on the nature and assumptions of your data, and the type of test or analytical task to perform.
Nov 20, 2020 · We have developed Cascabel, a scalable, flexible, and easy-to-use amplicon sequence data analysis pipeline, which uses Snakemake and a combination of existing and newly developed solutions for its computational steps.
- Alejandro Abdala Asbun, Marc A. Besseling, Sergio Balzano, Judith D. L. van Bleijswijk, Harry J. Wit...
- 10.3389/fgene.2020.489357
- 2020
- Front Genet. 2020; 11: 489357.
- What Does A Statistical Test do?
- When to Perform A Statistical Test
- Choosing A Parametric Test: Regression, Comparison, Or Correlation
- Choosing A Nonparametric Test
- Flowchart: Choosing A Statistical Test
- Other Interesting Articles
Statistical tests work by calculating a test statistic – a number that describes how much the relationship between variables in your test differs from the null hypothesis of no relationship. It then calculates a p value (probability value). The p-value estimates how likely it is that you would see the difference described by the test statistic if t...
You can perform statistical tests on data that have been collected in a statistically valid manner – either through an experiment, or through observations made using probability sampling methods. For a statistical test to be valid, your sample size needs to be large enough to approximate the true distribution of the population being studied. To det...
Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests. The most common types of parametric test include regression tests, comparison tests, and correlation tests.
Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.
This flowchart helps you choose among parametric tests. For nonparametric alternatives, check the table above.
If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.
Mar 6, 2023 · A positive likelihood ratio, or LR+, is the “probability that a positive test would be expected in a patient divided by the probability that a positive test would be expected in a patient without a disease.”.
- Jacob Shreffler, Martin R. Huecker
- 2023/03/06
- 2020
Apr 20, 2016 · In this post, I will explain t-values, t-distributions, and how t-tests use them to calculate probabilities and assess hypotheses. What Are t-Values? T-tests are called t-tests because the test results are all based on t-values. T-values are an example of what statisticians call test statistics.
Feb 3, 2022 · In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores. Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships. The independent variable is the cause.
Nov 28, 2022 · Learn to master each step of the ELISA protocol with the help of this step-by-step guide, ensuring that your next ELISA experiment yields accurate results. What is ELISA? The enzyme-linked immunosorbent assay (ELISA) is an antibody-based technique for the detection and quantification of target analytes in solution.
