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Related Concept Videos

Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.0K
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...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

229
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
229
What is a Hypothesis?01:14

What is a Hypothesis?

11.2K
A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...
11.2K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.1K
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...
4.1K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

158
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
158
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

26.6K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
26.6K

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Introduction to Statistical Hypothesis Testing in Nursing Research.

Courtney Keeler1, Alexa Colgrove Curtis

  • 1Courtney Keeler is an associate professor and Alexa Colgrove Curtis is assistant dean of graduate nursing and director of the MPH-DNP dual degree program, both at the University of San Francisco School of Nursing and Health Professions. Contact author: Courtney Keeler, ckeeler@usfca.edu . Bernadette Capili, PhD, NP-C, is the column coordinator: bcapili@rockefeller.edu . This manuscript was supported in part by grant No. UL1TR001866 from the National Institutes of Health's National Center for Advancing Translational Sciences Clinical and Translational Science Awards Program. The authors have disclosed no potential conflicts of interest, financial or otherwise.

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Summary

This article series provides nurses with essential knowledge for clinical research and evidence-based practice. It covers key concepts from research design to data interpretation, serving as a valuable resource.

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Area of Science:

  • Nursing Research
  • Evidence-Based Practice

Background:

  • This is the 16th article in an ongoing series focused on clinical research for nurses.
  • The series aims to equip nurses with fundamental research concepts and principles.

Purpose of the Study:

  • To serve as a comprehensive resource for nurses in understanding clinical research.
  • To elucidate the core concepts underpinning evidence-based practice.

Main Methods:

  • The series presents concepts sequentially, covering research design through data interpretation.
  • Each column focuses on specific elements crucial for research comprehension.

Main Results:

  • Nurses will gain a foundational understanding of research methodologies.
  • The series facilitates the application of research principles to clinical practice.

Conclusions:

  • This series is a vital educational tool for nurses advancing their research skills.
  • It supports the integration of evidence-based practice into daily nursing care.