CHE SIGNIFICA TYPE I ERROR IN INGLESE
Errori di tipo I e di tipo II
Nelle statistiche, un'ipotesi nullo è un'affermazione che la cosa che viene studiata non produce alcun effetto o non fa alcuna differenza. Un esempio di ipotesi nullo è l'affermazione "Questa dieta non ha alcun effetto sul peso della gente". Di solito un esperto mette un'ipotesi nullo con l'intento di rifiutarlo: cioè, intendo eseguire un esperimento che produce dati che dimostrano che la cosa in studio fa la differenza. Un errore di tipo I è il rifiuto errato di una vera ipotesi nullo. Per quanto riguarda l'ipotesi non nullo, rappresenta un falso positivo. Di solito un errore di tipo I porta a concludere che esiste un effetto presunto o una relazione quando in realtà non lo fa. Esempi di errori di tipo I includono un test che indica un paziente ad avere una malattia quando in realtà il paziente non ha la malattia, un allarme antincendio che scompare indica un incendio quando in realtà non c'è fuoco o un esperimento che indica che un trattamento medico Dovrebbe curare una malattia quando in realtà non lo fa. Un errore di tipo II è il mancato rifiuto di una falsa ipotesi nulla. Rispetto all'ipotesi non nullo, rappresenta un falso negativo.
definizione di type I error nel dizionario inglese
La definizione di errore di tipo I nel dizionario è l'errore di rifiutare l'ipotesi nulla quando è vera, la cui probabilità è il livello di significatività di un risultato.
PAROLE IN INGLESE ASSOCIATE CON «TYPE I ERROR»
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10 LIBRI IN INGLESE ASSOCIATI CON «TYPE I ERROR»
Scopri l'uso di
type I error nella seguente selezione bibliografica. Libri associati con
type I error e piccoli estratti per contestualizzare il loro uso nella letteratura.
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 NOTIZIE DOVE SI INCLUDE IL TERMINE «TYPE I ERROR»
Vedi di che si parla nei media nazionali e internazionali e come viene utilizzato il termine ino
type I error nel contesto delle seguenti notizie.
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, ott 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, giu 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, set 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, lug 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, apr 10»