The effect of outliers on confidence interval procedures for cost-effectiveness ratios Cost-effectiveness ratio (CER) is defined as the ratio of the difference in cost between a test and standard health care programme to the difference in benefit, respectively.
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An outlier stretches the interval because it increases the standard deviation An outlier compacts the interval because it increases the standard deviation An outlier stretches the interval because it decreases the standard deviation An outlier compacts the interval because it decreases the standard deviation.
Outliers can have a big impact on the confidence interval. This is hardly surprising since we use the mean and standard deviation to calculate the confidence interval.
Effect of outliers on a data set It increases the error variance and reduces the power of statistical tests. They can cause bias and/or influence estimates. They can also impact the basic assumption of regression as well as other statistical models.
In most practical circumstances an outlier decreases the value of a correlation coefficient and weakens the regression relationship, but it's also possible that in some circumstances an outlier may increase a correlation value and improve regression.
The CI based on the mean gives very long width when data consist of one outlier. Other CI's based on robust estimators give similar results for the case of no outlier. Although the CI based on mean yield a large width, the case of more than one outlier, robust CI are not affected by the outliers.
The mean is more sensitive to the existence of outliers than the median or mode.
Definition of outliers. An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal.
In a distribution of variables, outliers lie far from the majority of the other data points as the corresponding values are extreme or abnormal. The outliers contained in sample data introduce bias into statistical estimates such as mean values, leading to under- or over-estimated resulting values.
A value that "lies outside" (is much smaller or larger than) most of the other values in a set of data. For example in the scores 25,29,3,32,85,33,27,28 both 3 and 85 are "outliers".
The more extreme the outlier, the more the standard deviation is affected.
An outlier will always decrease a correlation coefficient.
An outlier can affect the mean of a data set by skewing the results so that the mean is no longer representative of the data set.