Examples of using Beta error in English and their translations into Portuguese
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Colloquial
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Official
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Medicine
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Financial
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
The maximum acceptable beta error was 0.20.
Beta error was set up at 0.20, so the power of the study was 80.
The Bonferroni strategy was applied to reduce the probability of beta error.
The beta error level or statistical power[1- beta] was 20.
The sample was calculated with an alpha error of 5% CI 95% and a beta error of 20% power 80.
Beta error was defined as 20% and the level of significance was 95% alpha error 5.
The sample size calculation was made based on a 5% level of significance p<0.05 and a beta error of 0.1.
Considering a beta error of 0.80 and an alpha error of 0.05, it was necessary to recruit at least 60 subjects per group.
For sample calculation of this study,we used an 80% test power and beta error value of 20.
Considering an alpha error of 0.05 and a beta error of 0.90, it was found that each group should be composed of 29 subjects.
It was estimated that 7 patients would be needed in each group to detect a difference of 5 dB,at an alpha error of 5% and a beta error of 20.
Considering a population of 2,672 women,an alpha error of 0.05 and a beta error of 0.15 IC 95%, a sample of 226 women was found.
Considering a beta error 20% and an expected CS frequency 6.3%, 393 individuals would be required to reach the statistical difference p< 0.05 concerning the mortality independent predictors.
Statistical power was determined a posteriori to establish the beta error in the detection of associations.
We believe that the possibility of a beta error is reduced, because the p values calculated for each moment were far away of the significance level.
By the sample target size for a bilateral hypothesis, an alpha risk of 5% anda statistical power of 80%, and a beta error of 20%, at least 282 cases must be studied.
For the calculation of the sample size n,considering a beta error of 20% and an expected frequency of 7.9%, at least 372 individuals were necessary to obtain a statistical difference p-value< 0.05.
To detect a 30% reduction in the incidence of shivering in the sufentanil group, the minimum number of patients in each group was calculated at 40,accepting a 5% alpha error and 20% beta error.
By the sample calculation of normally distributed variables,using an alpha error of 95% and a beta error of 80%, it was concluded that a minimum of 9 individuals should be recruited in each group.
Assuming a 5% alpha error and a 20% beta error, a sample of at least 18 patients in each group was estimated to be necessary to detect a 30% difference between the physiotherapy protocols evaluated.
We calculated 106 patients per group in order to detect a 20% difference in the change of levels of creatinine, assuming a mean value of 1.5 mg/dL after the procedure with an alpha error of 0.05 and a beta error of 0.2.
This may have occurred as a result of beta error, i.e. lack of statistical power, taking into consideration that PTSD commonly occurs in comorbidity with other psychiatric disorders, particularly major depression.
Correlations between pulmonary function and exercise test variables were assessed using Pearson's correlation coefficient r. Based on asample of 20 patients, accepting an alpha error of 5% and a beta error of 20%, correlations were accepted at r>= 0.6.
We calculated a sample containing 12 patients in each group as being sufficient to obtain a beta error of 80% and analpha error of 5%, estimating that there would be a reduction of HGS by 50% in the control group.
To demonstrate the representativeness of the present sample, after collection, sample size was calculated with the help of the StatCalc Epi Info program, release 3.5.1, using data from the study" Severe maternal morbidity and near miss events in a regional referral hospital", in which the prevalence of fetal and neonatal death was 12.5% among patientswith maternal near miss. Thus a sample of 168 patients was obtained, for an alpha error of 0.05 and a beta error of 0.20.
Using an alpha error of 5% and beta error of 20% power of 80% and an odds ratio of 5.7 based on the above mentioned study in Rio de Janeiro, we calculated a sample size of 304“cases” and 304“control cases”.
This can be explained by the small number of patients in each group, which can compromise statistical analysis, i.e.,this could be a Beta error, when both groups have a real difference, but the size of the cohort was not enough to demonstrate this difference.
To detect a27% difference in the accuracy of the clinically-estimated puncture levels between obese and non-obese patients, with proportion analysis test- accepting a 5% alpha error and 20% beta error- the size of de study population was calculated as 40 patients per group.
The size of the sample was calculated using a ratio of 1.5controls to 1 case, based on an alpha error of 5% and a beta error of 20%, sufficient for detecting an odds ratio OR equal to 1.7 and estimating an exposure of 25% among the controls. The original sample comprised 95 cases and 143 controls.
The number of patients was based on the literature. To detect a difference of approximately 30% between groups,assuming an alpha error of 0.5% and a beta error of 20%, we estimated at 20 the required the number of patients in each group totaling 40 patients.