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Related Concept Videos

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Statistical Significance01:37

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...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Statistical Hypothesis Testing01:16

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...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Introduction to Statistics01:17

Introduction to Statistics

The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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Published on: August 7, 2017

Presenting the statistical results.

K H Ng1, W C G Peh

  • 1Biomedical Imaging and Interventional Journal, Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia. dwlng@tm.net.my

Singapore Medical Journal
|February 19, 2009
PubMed
Summary

Clear reporting of statistical methods in scientific papers is crucial for data analysis and result verification. This guide offers essential tips for authors to accurately present their study

Area of Science:

  • Biostatistics
  • Scientific Communication
  • Data Analysis

Background:

  • Statistical methods are integral to summarizing and analyzing study data.
  • Accurate reporting ensures reproducibility and verification of research findings.
  • Current reporting practices may lack sufficient detail for full transparency.

Purpose of the Study:

  • To provide guidelines for authors on reporting statistical methods.
  • To enhance clarity and accuracy in the statistical sections of scientific papers.
  • To facilitate the verification of study results by knowledgeable readers.

Main Methods:

  • The article outlines fundamental principles for describing statistical methodologies.
  • Emphasis is placed on the level of detail required for data verification.

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  • Guidance covers the clear presentation of statistical analyses performed.
  • Main Results:

    • Clear statistical reporting aids in the accurate interpretation of study outcomes.
    • Detailed methods enable independent validation of the summarized data.
    • Improved reporting enhances the overall scientific rigor of publications.

    Conclusions:

    • Adherence to these guidelines will improve the quality of statistical reporting.
    • Accurate statistical reporting is essential for scientific integrity and progress.
    • Authors are encouraged to implement these recommendations for clearer communication.