No. Outliers are part of the data and do not affect them. They will, however, affect statistics based on the data and inferences based on the data.
When you are looking for a simple measure of the spread of the data, but one which is protected from the effects of extreme values (outliers).
If the data numbers are all really close together than no. But if the data has numbers; for example: 12,43,45,51,57,62,90 (12 and 90 are the outliers) which are really far aprt, than yes.
They are called extreme values or outliers.
Central tendency will only give you information on the location of the data. You also need dispersion to define the spread of the data. In addition, shape should also be part of the defining criteria of data. So, you need: location, spread & shape as best measures to define data.
Mostly through statistics, or summaries of the data set (depending on the type of data). There are many different statistical methods used to analyze the many different types of data that come from research studies or experiments. However if you just want a relatively quick and simplistic overview of a set of data than you should follow SOCS: Shape, Outliers, Center, Spread. Shape (the shape of the graphed data points) Outliers (any data points that fall outside the realm of "normal") Center (where the data points are mostly centered around) and Spread (the range of the data points). This should give you some immediate conclusions from your data.
There is no limit to the number of outliers there can be in a set of data.
No. Outliers are part of the data and do not affect them. They will, however, affect statistics based on the data and inferences based on the data.
When you are looking for a simple measure of the spread of the data, but one which is protected from the effects of extreme values (outliers).
an outliers can affect the symmetry of the data because u can still move around it
In chemistry, outliers are data points that deviate significantly from the rest of the data set. Outliers can result from measurement errors, experimental uncertainties, or unexpected reactions. It is important to identify and address outliers in data analysis to ensure accurate and reliable results.
It is not.
Outliers
If the data numbers are all really close together than no. But if the data has numbers; for example: 12,43,45,51,57,62,90 (12 and 90 are the outliers) which are really far aprt, than yes.
Go into your data to determine which values are outliers and if they're significant and random (not an apparent group), eliminate them. This will take them out of your boxplot.
They are called extreme values or outliers.
Mode: Data are qualitative or categoric. Median: Quantitative data with outliers - particularly if the distribution is skew. Mean: Quantitative data without outliers, or else approx symmetrical.