5 results on '"Type 2 error"'
Search Results
2. The Most Basic Concepts in Biostatistics
- Author
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Palmas, Walter R. and Palmas, Walter R.
- Published
- 2023
- Full Text
- View/download PDF
3. Why and When Statistics is Required, and How to Simplify Choosing Appropriate Statistical Techniques During Ph.D. Program in India?
- Author
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H. R. Ganesha and P. S. Aithal
- Subjects
Coursework ,History ,Polymers and Plastics ,Null Hypothesis ,Normal Distribution ,Research Methodology ,Median ,Non-parametric test ,Skewness ,Industrial and Manufacturing Engineering ,Measures of Dispersion ,Ph.D ,Type 1 Error ,FOS: Mathematics ,Bell Curve ,Mean ,Postmodernism ,Inferential Statistics ,Business and International Management ,Alpha ,Kurtosis ,Descriptive Statistics ,Significance Testing ,Statistics ,PhD ,Parametric Test ,Beta ,JASP ,Hypothesis Testing ,General Medicine ,Range ,Statistical Techniques ,Type 2 Error ,Coefficient of Variation ,Alternate Hypothesis ,Research Design ,Significance Level ,Standard Deviation ,Statistical Significance ,Mode ,Doctoral Research ,Research Hypothesis ,Measures of Central Tendency - Abstract
Purpose: The purpose of this article is to explain the key reasons for the existence of statistics in doctoral-level research, why and when statistical techniques are to be used, how to statistically describe the units of analysis/samples, how to statistically describe the data collected from units of analysis/samples; how to statistically discover the relationship between variables of the research question; a step-by-step process of statistical significance/hypothesis test, tricks for selecting an appropriate statistical significance test, and most importantly which is the most user-friendly and free software for carrying out statistical analyses. In turn, guiding Ph.D. scholars to choose appropriate statistical techniques across various stages of the doctoral-level research process to ensure a high-quality research output. Design/Methodology/Approach: Postmodernism philosophical paradigm; Inductive research approach; Observation data collection method; Longitudinal data collection time frame; Qualitative data analysis. Findings/Result: As long as the Ph.D. scholars can understand i) they need NOT be an expert in Mathematics/Statistics and it is easy to learn statistics during Ph.D.; ii) the difference between measures of central tendency and dispersion; iii) the difference between association, correlation, and causation; iv) difference between null and research/alternate hypotheses; v) difference between Type I and Type II errors; vi) key drivers for choosing a statistical significance test; vi) which is the best software for carrying out statistical analyses. Scholars will be able to (on their own) choose appropriate statistical techniques across various steps of the doctoral-level research process and comfortably claim their research findings. Originality/Value: There is a vast literature about statistics, probability theory, measures of central tendency and dispersion, formulas for finding the relationship between variables, and statistical significance tests. However, only a few have explained them together comprehensively which is conceivable to Ph.D. scholars. In this article, we have attempted to explain the reasons for the existence, objectives, purposes, and essence of ‘Statistics’ briefly and comprehensively with simple examples and tricks that would eradicate fear among Ph.D. scholars about ‘Statistics’. Paper Type: Conceptual.
- Published
- 2022
- Full Text
- View/download PDF
4. Biostatistics series module 5: Determining sample size
- Author
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Avijit Hazra and Nithya J Gogtay
- Subjects
business.industry ,Sample (material) ,030229 sport sciences ,Dermatology ,Effect size ,lcsh:RL1-803 ,Statistical power ,sample size ,Type 2 error ,Insensitivity to sample size ,power ,030207 dermatology & venereal diseases ,03 medical and health sciences ,Type 1 error ,0302 clinical medicine ,Sample size determination ,Statistics ,lcsh:Dermatology ,Z-test ,Medicine ,IJD® Module on Biostatistics and Research Methodology for the Dermatologist - MODULE EDITOR: SAUMYA PANDA ,business ,Null hypothesis ,Type I and type II errors ,Statistical hypothesis testing - Abstract
Determining the appropriate sample size for a study, whatever be its type, is a fundamental aspect of biomedical research. An adequate sample ensures that the study will yield reliable information, regardless of whether the data ultimately suggests a clinically important difference between the interventions or elements being studied. The probability of Type 1 and Type 2 errors, the expected variance in the sample and the effect size are the essential determinants of sample size in interventional studies. Any method for deriving a conclusion from experimental data carries with it some risk of drawing a false conclusion. Two types of false conclusion may occur, called Type 1 and Type 2 errors, whose probabilities are denoted by the symbols σ and β. A Type 1 error occurs when one concludes that a difference exists between the groups being compared when, in reality, it does not. This is akin to a false positive result. A Type 2 error occurs when one concludes that difference does not exist when, in reality, a difference does exist, and it is equal to or larger than the effect size defined by the alternative to the null hypothesis. This may be viewed as a false negative result. When considering the risk of Type 2 error, it is more intuitive to think in terms of power of the study or (1 − β). Power denotes the probability of detecting a difference when a difference does exist between the groups being compared. Smaller α or larger power will increase sample size. Conventional acceptable values for power and α are 80% or above and 5% or below, respectively, when calculating sample size. Increasing variance in the sample tends to increase the sample size required to achieve a given power level. The effect size is the smallest clinically important difference that is sought to be detected and, rather than statistical convention, is a matter of past experience and clinical judgment. Larger samples are required if smaller differences are to be detected. Although the principles are long known, historically, sample size determination has been difficult, because of relatively complex mathematical considerations and numerous different formulas. However, of late, there has been remarkable improvement in the availability, capability, and user-friendliness of power and sample size determination software. Many can execute routines for determination of sample size and power for a wide variety of research designs and statistical tests. With the drudgery of mathematical calculation gone, researchers must now concentrate on determining appropriate sample size and achieving these targets, so that study conclusions can be accepted as meaningful.
- Published
- 2016
5. The use and misuse of p values and related concepts.
- Author
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Brereton, Richard G.
- Subjects
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FALSE positive error , *STATISTICAL hypothesis testing , *NULL hypothesis , *CONCEPTS , *FAKE news - Abstract
The paper describes historic origins of p values via the work of Fisher, and the competing approach by Neyman and Pearson. Concepts of type 1 and type 2 errors, false positive rates, power, and prevalence are also defined, and the merger of the two approaches via the Null Hypothesis Significance Test. The relationship between p values and false detection rate is discussed. The reproducibility of p values is described. The current controversy over the use of p values and significance tests is introduced. • Historical review of the concept of p values. • Relationship between common concepts such as False Positive Rates and p values and type 1 and type 2 errors. • Reproducibility of p values. • Current controversy over use of p values. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
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