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Related Concept Videos

P-value01:10

P-value

9.4K
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...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

7.2K
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 Significance01:37

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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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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...
5.8K
Fisher's Exact Test01:08

Fisher's Exact Test

1.4K
Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
1.4K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.1K
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...
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How do I interpret a p value?

Sheila F O'Brien1, Lori Osmond1, Qi-Long Yi1

  • 1Canadian Blood Services, Ottawa, Ontario, Canada.

Transfusion
|November 8, 2015
PubMed
Summary
This summary is machine-generated.

A p-value helps interpret research, with values below 0.05 often indicating statistical significance. However, statistical significance doesn't always mean clinical significance for patient outcomes.

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Research Methodology

Background:

  • P-values are crucial for interpreting statistical results in research.
  • Understanding p-values is essential for drawing valid conclusions from data analysis.

Purpose of the Study:

  • To explain the theory and interpretation of p-values.
  • To highlight common errors in p-value interpretation.
  • To differentiate statistical significance from clinical significance.

Main Methods:

  • Explanation of p-value theory using sample mean comparison.
  • Illustrative examples of hypothesis testing and null hypothesis rejection.

Main Results:

  • A p-value less than 0.05 typically suggests statistical significance, allowing rejection of the null hypothesis.
  • A statistically significant finding does not guarantee a real difference or clinical importance.

Conclusions:

  • P-values are valuable but require careful interpretation to avoid misrepresentation.
  • Distinguishing statistical from clinical significance is vital for patient care and research application.