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By definition, an outlier will not have the same value as other data points in the dataset. So, the correct question is "What is the effect of an outlier on a dataset's mean." The answer is that the outlier moves the mean away from the value of the other 49 identical values. If the outlier is the "high tail" the mean is moved to a higher value. If the outlier is a "low tail" the mean is moved to a lower value.

Q: If an outlier is included with a sample of 50 values all of which are the same what is the effect of the outlier on the mean?

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I have included a link on outliers. The definition as given in Wikipedia is similar to those in other textbooks: In statistics, an outlier is an observation that is numerically distant from the rest of the data. It is not a rigorous definition, because "numerically distant" adds an element of subjective judgement. Values which are far removed from the typical range (or grouping) of a sample are not necessarily erroneous data. For this reason, the use of adjectives that add some doubt as "apparent" or "possible" to the term outlier is correct. See related link for examples of outliers.

Given that the study manager wants the QC efforts to be focused on selecting outlier values, whose method is a better way of selecting the sample

An outlier

It is a value calculated from the sample values only.It is a value calculated from the sample values only.It is a value calculated from the sample values only.It is a value calculated from the sample values only.

Not sure about an outlair, but an outlier in a set of values is one that is significantly smaller or greater than the others. There is no formally agreed definition for an outlier.

Related questions

0s are not the outlier values

The average - arithmetic mean - is calculated as the sum of the values divided by the number of values. Compared with other observations, the outlier makes an abnormally small or large contribution to the sum, while making the same contribution to the count of observations.

I have included a link on outliers. The definition as given in Wikipedia is similar to those in other textbooks: In statistics, an outlier is an observation that is numerically distant from the rest of the data. It is not a rigorous definition, because "numerically distant" adds an element of subjective judgement. Values which are far removed from the typical range (or grouping) of a sample are not necessarily erroneous data. For this reason, the use of adjectives that add some doubt as "apparent" or "possible" to the term outlier is correct. See related link for examples of outliers.

If a data set has an outlier, you would normally deal with it by omitting it from the average of the other values.

Given that the study manager wants the QC efforts to be focused on selecting outlier values, whose method is a better way of selecting the sample

Outlier

An outlier

It is a value calculated from the sample values only.It is a value calculated from the sample values only.It is a value calculated from the sample values only.It is a value calculated from the sample values only.

Not sure about an outlair, but an outlier in a set of values is one that is significantly smaller or greater than the others. There is no formally agreed definition for an outlier.

An outlier is a value in a data set that is numerically distant from the other values. This definition is not rigorous, as I have not clearly identified a test to determine a value which is "numerically distant." However, in many situations, the definition is sufficient to identify values outside of an observable trend or grouping. The importance of the outlier depends on the statistics being calculated. If we are calculating the median price of a home, and we include a one huge mansion, the median will only be slightly effected. However, if we are calculating the cost of the most expensive houses, say in the top 10% percentile, the outlier will have a significant effect. See related link on outliers.

an outlier

The value that is not typical of most other values in a data set is an Outlier.