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P-value

P-value

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...

Statistical Significance

Statistical Significance

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...

Decision Making: P-value Method

Decision Making: P-value Method

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...

Significance Testing: Overview

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...

Bonferroni Test

Bonferroni Test

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...

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

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Editorial Commentary: "There, It Fits!"-Justifying Nonsignificant P Values.

Walter R Lowe¹

¹University of Texas.

Arthroscopy : the Journal of Arthroscopic & Related Surgery : Official Publication of the Arthroscopy Association of North America and the International Arthroscopy Association

|November 7, 2016

Summary

Value-based medicine drives comparative clinical studies. Focus on meaningful study designs and interpret nonsignificant P values appropriately, rather than viewing them as failures.

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Area of Science:

Health Economics
Clinical Research Methodology
Medical Statistics

Background:

Value-based medicine is increasingly emphasized, promoting comparative clinical outcome studies.
Significant advancements have been made in clinical research over recent decades.
There is a growing need to evaluate the quality of study designs in clinical research.

Discussion:

The interpretation of statistical significance, particularly nonsignificant P values, requires careful consideration.
Nonsignificant P values should not be automatically equated with study failure.
The focus should shift towards the clinical relevance and implications of findings, irrespective of statistical significance.

Key Insights:

Meaningful study designs are crucial for generating reliable and clinically relevant outcomes.
Overemphasis on statistical significance can overshadow important findings from well-designed studies.
A nuanced understanding of P values is essential for accurate scientific interpretation.

Outlook:

Future research should prioritize robust study designs that align with value-based healthcare principles.
Promoting a culture that values methodological rigor over mere statistical significance is vital.
Continued dialogue on the interpretation of study results will enhance the quality of medical evidence.