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Related Concept Videos

P-value01:10

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 Method01:09

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...
Critical Values01:31

Critical Values

A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
Critical Region, Critical Values and Significance Level01:16

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...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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...
Bonferroni Test01:10

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...

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Methodology to Metabolically Inactivate Bacteria for Caenorhabditis elegans Research
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Mind your p values.

Daniel C Jupiter1

  • 1Department of Surgery, Texas A&M Health Science Center, College of Medicine, Temple, TX, USA. djupiter@sw.org

The Journal of Foot and Ankle Surgery : Official Publication of the American College of Foot and Ankle Surgeons
|November 1, 2012
PubMed
Summary
This summary is machine-generated.

This study clarifies the logical structure of medical research findings. Understanding how evidence proves a case is essential for interpreting scientific arguments.

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Area of Science:

  • Medical research methodology
  • Scientific argumentation

Background:

  • Medical studies aim to present convincing evidence.
  • The logical progression of proof in research can be complex.

Purpose of the Study:

  • To simplify and explain the logic used in medical studies.
  • To enhance reader comprehension of scientific proof.

Main Methods:

  • Conceptual analysis of research argumentation.
  • Explanation of logical principles in evidence presentation.

Main Results:

  • Identification of key components in constructing a scientific case.
  • Demystification of the process by which evidence supports a hypothesis.

Conclusions:

  • A clearer understanding of medical study logic aids critical appraisal.
  • Improved interpretation of research findings is achievable through simplified explanations.