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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...

Critical Region, Critical Values and Significance Level

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...

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 unlikely...

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 have a...

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...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

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Colletotrichum fioriniae Development in Water and Chloroform-based Blueberry and Cranberry Floral Extracts

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P-value: What is and what is not.

Kiarash Tanha¹, Neda Mohammadi², Leila Janani¹

¹Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.

Medical Journal of the Islamic Republic of Iran

|February 16, 2018

Summary

Misinterpreting p-values is a growing problem. This study clarifies the definition and correct interpretation of p-values, stressing their accurate use in research.

Keywords:

Hypothesis testing P-value Statistical significance

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Area of Science:

Statistics
Scientific Methodology

Background:

The misinterpretation and misuse of p-values have escalated over decades.
The American Statistical Association issued a statement in March 2016 to address concerns regarding p-value interpretation.

Purpose of the Study:

To provide a clear definition of the p-value.
To discuss the appropriate use and interpretation of p-values.
To highlight the critical importance of accurate p-value interpretation in scientific research.

Main Methods:

Literature review of statistical best practices.
Analysis of common p-value misinterpretations.
Synthesis of guidelines for accurate p-value usage.

Main Results:

P-values are often misunderstood as the probability of a hypothesis being true.
Accurate interpretation requires considering effect size and prior evidence.
Misuse can lead to flawed conclusions and irreproducible research.

Conclusions:

Emphasizing the accurate interpretation of p-values is crucial for scientific integrity.
Clearer statistical reporting standards are needed.
Researchers must be educated on the proper understanding and application of p-values.