Related Experiment Video
Updated: Dec 22, 2025
06:58
Author Spotlight: Advancing Caenorhabditis elegans Research Using Paraformaldehyde-Treated Bacteria
Published on: July 28, 2023
3.0K
In Defense of P Values
1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
JNCI Cancer Spectrum
|May 7, 2020
Summary
Statistical significance tests and P values are debated, but remain valuable tools. Correctly applied, statistical testing supports the scientific method, especially with increased transparency and reproducibility.
Area of Science:
- Statistics
- Scientific Methodology
- Research Integrity
Background:
- A recent controversy questions the use of statistical significance tests and P values.
- Prominent statisticians have suggested discouraging or eliminating their use, causing confusion among researchers.
- Criticism stems from concerns that P values encourage poor scientific practices and lead to inaccurate findings.
Purpose of the Study:
- To address the controversy surrounding statistical significance tests and P values.
- To clarify the role of statistical methods in scientific research.
- To emphasize the importance of the scientific method's truth-seeking tradition.
Main Methods:
- Commentary and discussion of existing statistical practices and criticisms.
- Analysis of the historical context of P value controversy.
- Exploration of human nature's influence on data interpretation and presentation.
Main Results:
- The core issue is attributed to human nature and the tendency to favor one's own theories.
- A 'celebrity culture' in academia, journals, and media exacerbates the problem.
- Initiatives promoting transparency, reproducibility, and replicability are crucial for reinforcing scientific integrity.
Conclusions:
- Statistical testing, when used correctly, retains a vital role in scientific research.
- Reinforcing the truth-seeking tradition of science is essential.
- Addressing human biases is key to overcoming challenges associated with statistical interpretation.
Related Concept Videos
01:09Decision Making: P-value Method
6.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.7K
01:10P-value
8.4K
P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value. P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
P-value stands for the probability value. P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
8.4K
01:24Testing a Claim about Population Proportion
3.8K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.8K
01:14Decision Making: Traditional Method
5.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.0K
01:10Bonferroni Test
3.2K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.2K
01:50Statistical Significance
20.9K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
20.9K