The mean may be a good measure but not if the data distribution is very skewed.
The outlier 57 affects the measure of central tendency by increasing the numbers and making the problems difficult.
0s are not the outlier values
The answer depends on the nature of the outlier. Removing a very small outlier will increase the mean while removing a large outlier will reduce the mean.
there is no outlier because there isn't a data set to go along with it. so theres no outlier
the most common cause of an outlier is an error in the recording of data.
The median.
The outlier 57 affects the measure of central tendency by increasing the numbers and making the problems difficult.
The answer is outlier
mean
The median, as long as you don't want to do any serious statistical testing.
An outlier can significantly impact the median by pulling it towards the extreme value of the outlier, especially when the dataset is small. This can distort the central tendency measure that the median represents and provide a misleading representation of the typical value in the dataset.
When a data set has an outlier, the best measure of center to use is the median, as it is less affected by extreme values compared to the mean. For measure of variation (spread), the interquartile range (IQR) is preferable, since it focuses on the middle 50% of the data and is also resistant to outliers. Together, these measures provide a more accurate representation of the data's central tendency and variability.
When there are no outliers in a data set, the mean is typically the best measure of central tendency. This is because the mean takes into account all values in the data set, providing a comprehensive average. It reflects the overall distribution of the data more accurately when the values are evenly spread without extreme variations. In such cases, the median and mode may not provide as much insight into the data's overall behavior.
An outlier is a number in a data set that is not around all the other numbers in the data. It will always affect the average; sometimes raising the average to a number higher than it should be, or lowering the average to something not reasonable. Example: Data Set - 2,2,3,5,6,1,4,9,31 Obviously 31 is the outlier. If you were to average these numbers it would be something greater than most of the numbers in your set due to the 31.
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
mean
An outlier is 1.5 times the mean, when you are taking an average it may give an inaccurate representation of the data. It usually does not affect the median.* * * * * The above definition of an outlier is total rubbish! It is necessary to have a measure of the central tendency (mean or median) AND spread (standard deviation or inter quartile range - IQR) to define an outlier.If Q1 and Q3 are the lower and upper quartiles, then outliers are normally defined as observations lying below Q1 - k*IQR or above Q3 + k*IQR. There is no universally agreed definition of outliers and hence no fixed value for k. But k = 1.5 is often used.