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Related Concept Videos

P-value01:10

P-value

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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...
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Significance Testing: Overview01:04

Significance Testing: Overview

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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...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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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...
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Statistical Significance01:50

Statistical Significance

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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...
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Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

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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...
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Bonferroni Test

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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...
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Understanding p-values and significance.

Naomi Altman1, Martin Krzywinski2

  • 1Department of Statistics, The Pennsylvania State University, State College, PA, USA.

Laboratory Animals
|September 24, 2024
PubMed
Summary
This summary is machine-generated.

P-values and effect sizes help assess experimental importance. Avoid selection bias by following principled p-value practices and avoiding questionable data analysis methods to ensure valid interpretation of research findings.

Keywords:
Statistics < TECHNIQUES

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Area of Science:

  • Statistics
  • Biostatistics
  • Scientific Methodology

Background:

  • P-values and effect sizes are crucial for evaluating experimental outcomes.
  • Interpretation of these metrics can be compromised by various forms of selection bias.
  • Common sources of bias include multiple hypothesis testing and post-hoc data selection.

Purpose of the Study:

  • To provide a clear introduction to the appropriate use of p-values.
  • To guide non-specialists in understanding and applying p-values correctly.
  • To identify and caution against problematic practices in statistical analysis.

Main Methods:

  • Review of statistical principles for hypothesis testing.
  • Explanation of selection bias and its impact on p-value interpretation.
  • Guidance on best practices for data analysis and result selection.

Main Results:

  • Identification of specific scenarios where p-value interpretation is vulnerable to bias.
  • Demonstration of how multiple testing and informal data selection can invalidate results.
  • Outline of principled approaches to using p-values effectively.

Conclusions:

  • Adherence to principled statistical practices is essential for valid interpretation of experimental results.
  • Awareness and avoidance of selection bias are critical for researchers.
  • Proper use of p-values enhances the reliability and credibility of scientific findings.