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.
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.
Outliers will make give the graph a long tail (or tails). Overall, the graph will be flatter and wider.
In statistics, outliers are values outside the norm relative to the rest of collected data. Many times they can skew the results and distort the interpretation of data. They may or may not indicate anything significant; they might just be an anomalous data point that is not significant. It is difficult to tell.
The range is very sensitive to outliers. Indeed if there are outliers then the range will be unrelated to any other elements of the sample.
There is no limit to the number of outliers there can be in a set of data.
an outliers can affect the symmetry of the data because u can still move around it
It is not.
Outliers
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.
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.
Data points that do not fit with the rest of a data set are known as outliers. These values are significantly different from the majority of the data, either much higher or lower, and can skew statistical analyses. Outliers may arise from variability in the data, measurement errors, or they could indicate a novel phenomenon worth investigating. Identifying and understanding outliers is crucial for accurate data interpretation.
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.
To report outliers in a study or experiment, identify data points that are significantly different from the rest of the data. Clearly state the criteria used to define outliers and explain how they were handled in the analysis. This helps ensure the accuracy and reliability of the results.
I think it means that our data includes outliers.