«TYPE I ERROR»に関連する英語の本
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type I errorの使いかたを見つけましょう。
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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
...
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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, 10月 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, 6月 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, 9月 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, 7月 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, 4月 10»
