True. I've included a link which will help you understand why.
when there are extreme values in the data
MEDIANUse the median to describe the middle of a set of data that does have an outlier.Advantages:• Extreme values (outliers) do not affect the median as strongly as they do the mean.• Useful when comparing sets of data.• It is unique - there is only one answer.Disadvantages:• Not as popular as mean.
Extreme high or low values in a data set, known as outliers, can significantly skew the mean. For instance, a few very high values can inflate the mean, making it higher than the central tendency of the majority of the data. Conversely, extreme low values can drag the mean down, misrepresenting the typical value of the dataset. This sensitivity makes the mean less reliable as a measure of central tendency when outliers are present.
The advantage of harmonic mean is that it is used to solve situations in which there are extreme data values to true picture. The disadvantage of it is that it can be time consuming to evaluate the data.
When subtracting values from the mean, you typically calculate the differences as either positive or negative based on whether the individual values are above or below the mean. Positive differences indicate values that are greater than the mean, while negative differences represent values that are less than the mean. This approach helps in understanding the distribution of data around the mean and can be useful in statistical analyses, such as calculating variance or standard deviation.
when there are extreme values in the data
MEDIANUse the median to describe the middle of a set of data that does have an outlier.Advantages:• Extreme values (outliers) do not affect the median as strongly as they do the mean.• Useful when comparing sets of data.• It is unique - there is only one answer.Disadvantages:• Not as popular as mean.
Extreme high or low values in a data set, known as outliers, can significantly skew the mean. For instance, a few very high values can inflate the mean, making it higher than the central tendency of the majority of the data. Conversely, extreme low values can drag the mean down, misrepresenting the typical value of the dataset. This sensitivity makes the mean less reliable as a measure of central tendency when outliers are present.
mean
The advantage of harmonic mean is that it is used to solve situations in which there are extreme data values to true picture. The disadvantage of it is that it can be time consuming to evaluate the data.
The mean is used to measure the average of a set of values, especially when the data is normally distributed. The median is used to find the middle value of a dataset when there are extreme values or outliers present, as it is less affected by extreme values.
MEANUse the mean to describe the middle of a set of data that does not have an outlier.Advantages:• Most popular measure in fields such as business, engineering and computer science.• It is unique - there is only one answer.• Useful when comparing sets of data.Disadvantages:• Affected by extreme values (outliers)
You would use the median if the data were very skewed, with extreme values.
The median is advantageous because it is not influenced by extreme values, making it a robust measure of central tendency for skewed data sets. It is also easy to interpret and calculate. However, the median may not accurately represent the true center of a dataset if the data is heavily skewed or if there are outliers present. Additionally, the median may not be as efficient as the mean for certain statistical calculations due to its lack of sensitivity to all data points.
Well, honey, the advantage of using the harmonic mean is that it gives more weight to smaller values, which can be helpful when dealing with rates or ratios. On the flip side, it can be heavily influenced by outliers, so if you've got some wild numbers in your data, the harmonic mean might not be the best choice. Just remember, there's no one-size-fits-all when it comes to statistics, so choose your mean wisely!
It is the mean average of number of collected data values.
skewed