QUÉ SIGNIFICA TYPE I ERROR EN INGLÉS
Errores de tipo I y de tipo II
En las estadísticas, una hipótesis nula es una afirmación de que lo que se estudia no produce ningún efecto o no hace ninguna diferencia. Un ejemplo de una hipótesis nula es la afirmación "Esta dieta no tiene ningún efecto sobre el peso de las personas". Por lo general, un experimentador enmarca una hipótesis nula con la intención de rechazarla: es decir, la intención de ejecutar un experimento que produce datos que demuestran que la cosa bajo estudio hace una diferencia. Un error de tipo I es el rechazo incorrecto de una hipótesis nula verdadera. Con respecto a la hipótesis no nula, representa un falso positivo. Por lo general, un error de tipo I lleva a concluir que existe un supuesto efecto o relación cuando en realidad no lo hace. Ejemplos de errores de tipo I incluyen una prueba que muestra que un paciente tiene una enfermedad cuando de hecho el paciente no tiene la enfermedad, una alarma de incendio que apaga indicando un incendio cuando de hecho no hay fuego o un experimento que indica que un tratamiento médico Debe curar una enfermedad cuando en realidad no lo hace. Un error de tipo II es la falta de rechazo de una falsa hipótesis nula. Con respecto a la hipótesis no nula, representa un falso negativo.
definición de type I error en el diccionario inglés
La definición de error de tipo I en el diccionario es el error de rechazar la hipótesis nula cuando es verdadera, cuya probabilidad es el nivel de significación de un resultado.
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10 LIBROS DEL INGLÉS RELACIONADOS CON «TYPE I ERROR»
Descubre el uso de
type I error en la siguiente selección bibliográfica. Libros relacionados con
type I error y pequeños extractos de los mismos para contextualizar su uso en la literatura.
1
Research Methods and Statistics: A Critical Thinking Approach
Remember, however, that when we reject the null hypothesis, we could be
correct in our decision, or we could be making a Type I error. Maybe the null
hypothesis is true, and this is one of those 5 or less times out of 100 when the
observed ...
2
Experimental Design and Data Analysis for Biologists
The different approaches for dealing with the increased probability of a Type I
error in multiple testing situations are based on how the Type I error rate for each
test (the comparison-wise Type I error rate) is reduced to keep the family-wise ...
Gerald Peter Quinn, Michael J. Keough,
2002
3
Introduction to Research in Education
The consequences of a Type I error are generally considered more serious than
the consequences of a Type II error, although there are certainly exceptions.
LEVEL OF SIGNIFICANCE Recall that all scientific conclusions are statements
that ...
Donald Ary, Lucy Jacobs, Asghar Razavieh,
2009
4
Business Statistics: Contemporary Decision Making
In particular, two types of errors can be made in testing hypotheses: Type I error
and Type II error. A Type I error is committed by rejecting a true null hypothesis.
With a Type I error, the null hypothesis is true, but the business researcher
decides ...
5
Statistical Methods for Health Care Research
The incorrect response would be to reject a true null hypothesis (type I error). If
H0 is false and we reject it, we have responded correctly. The wrong response
would be to accept a false null hypothesis (type II error). Suppose you compared
...
Barbara Hazard Munro,
2005
6
Statistics for Evidence-Based Practice and Evaluation
When a finding is statistically significant, the p-value is the probability that the
statistical conclusion (rejecting the null hypothesis and thus risking a Type I error)
is invalid. When a finding is not statistically significant, the probability that the ...
7
Research Methods: A Modular Approach
we have observed a significant difference in IQ scores between the sample and
the population. However, when we reject the null hypothesis, we could be correct
in our decision or we could be making a Type I error. Maybe the null hypothesis ...
8
Sample Size Calculations in Clinical Research
Precision analysis and power analysis for sample size determination are usually
performed by controlling type I error (or confidence level) and type II error (or
power), respectively. In what follows, we will first introduce the concepts of type I
and ...
Shein-Chung Chow, Hansheng Wang, Jun Shao,
2003
9
Research Methodology: Methods and Techniques
The former is known as Type I error and the latter as Type II error. In other words,
Type I error means rejection of hypothesis which should have been accepted and
Type II error means accepting the hypothesis which should have been rejected ...
10
Introductory Econometrics: A Modern Approach
In hypothesis testing, we can make two kinds of mistakes. First, we can reject the
null hypothesis when it is in fact true. This is called a Type I error. In the election
example, a Type I error occurs if we reject H0 when the true proportion of people
...
5 NOTICIAS EN LAS QUE SE INCLUYE EL TÉRMINO «TYPE I ERROR»
Conoce de qué se habla en los medios de comunicación nacionales e internacionales y cómo se emplea el término
type I error en el contexto de las siguientes noticias.
Trouble at the lab
Scientists divide errors into two classes. A type I error is the mistake of thinking something is true when it is not (also known as a “false positive”). A type II error is ... «The Economist, Oct 13»
4 Rules for hiring your startup's next great employee (and avoiding …
In statistics there are two types of general errors: Type I and Type II (got to love a statistician's sense of brevity!). A Type I error is a false positive. Type II error is a ... «The Rude Baguette, Jun 13»
Go Figure: Why we think rituals can influence results
This error is so fundamental that it's known as the Type I error. Though the Type II error is pretty fundamental too. You might even be suffering from apophenia, ... «BBC News, Sep 11»
Federal Agencies: Waning Integrity, Dwindling Trust
They can err by permitting something bad to happen (approving a harmful product, a Type I error in risk-analysis parlance) or by preventing something good ... «Forbes, Jul 10»
A Lesson in Inferential Statistics: Type I vs. Type II Errors
Then there is the error of false negative of thinking that the danger is not there when it is. Statisticians call the former type of errors “Type I errors” and the latter ... «Psychology Today, Abr 10»