An outlier can affect the mean of a data set by skewing the results so that the mean is no longer representative of the data set.

The effect of removing one outlier data point from the set No matter what value we add to the set, the mean, median, and mode will shift by that amount but the range and the IQR will remain the same.

Removal of outliers creates a normal distribution in some of my variables, and makes transformations for the other variables more effective.

Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. Outliers increase the variability in your data, which decreases statistical power. Consequently, excluding outliers can cause your results to become statistically significant.

The outlier decreases the mean so that the mean is a bit too low to be a representative measure of this student’s typical performance. This makes sense because when we calculate the mean, we first add the scores together, then divide by the number of scores. Every score therefore affects the mean.

The median is the middle value in a data set. It is not affected by outliers. The mode is the most common value in a data set. It is not affected by outliers.

The Interquartile Range is Not Affected By Outliers Since the IQR is simply the range of the middle 50% of data values, it’s not affected by extreme outliers.

median

The standard deviation is calculated using every observation in the data set. Consequently, it is called a sensitive measure because it will be influenced by outliers.

Also, we identify outliers in data sets. A range is the positive difference between the largest and smallest values in a data set. An outlier is a value that is much smaller or larger than the other data values. It is possible for a data set to have one or more outliers.

Once outliers have been identified they can be looked at more closely and can lead to some unexpected knowledge, and can show more about individuals that do not fit the ‘norm’. They can also be used to reveal errors within the research model.

1 : a person whose residence and place of business are at a distance His house was a place of refuge for outliers. 3a : a statistical observation that is markedly different in value from the others of the sample Values that are outliers give disproportionate weight to larger over smaller values. …

An “outlier” is anyone or anything that lies far outside the normal range. In business, an outlier is a person dramatically more or less successful than the majority.

n. an extreme observation or measurement, that is, a score that significantly differs from all others obtained. If most individuals obtained scores near the average IQ of 100 yet one person had an IQ of 150, the latter score would be an outlier. …

In addition to being entertained, Outliers shares valuable lessons that entrepreneurs and top performers can learn to help them succeed….The key determining factors of success examined in Outliers are:

1. Opportunity.
2. Timing.
3. Upbringing.
4. Effort.
5. Meaningful work.
6. Legacy.

If you drop outliers:

1. Trim the data set, but replace outliers with the nearest “good” data, as opposed to truncating them completely. (This called Winsorization.)
2. Replace outliers with the mean or median (whichever better represents for your data) for that variable to avoid a missing data point.

Malcolm Gladwell’s primary objective in Outliers is to examine achievement and failure as cultural phenomena in order to determine the factors that typically foster success.

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An outlier is a value in a data set that is very different from the other values. That is, outliers are values unusually far from the middle. In most cases, outliers have influence on mean , but not on the median , or mode .

In Outlier Leadership, Dr. Christopher Brazzle helps leaders with simple leadership principles of leaders who are set apart from the crowd.

An outlier is an object that deviates significantly from the rest of the objects. They can be caused by measurement or execution error. The analysis of outlier data is referred to as outlier analysis or outlier mining.

Noise may be present as deviations in attribute values or even as missing values. Low data quality and the presence of noise bring a huge challenge to outlier detection. They can distort the data, blurring the distinction between normal objects and outliers.

Identification of potential outliers is important for the following reasons. An outlier may indicate bad data. For example, the data may have been coded incorrectly or an experiment may not have been run correctly. Outliers may be due to random variation or may indicate something scientifically interesting.

If you go by standard convention removing an outlier will cause the standard deviation to decrease. In general though, an outlier is a data point that is extreme for the distribution of the observed data.

There are no outliers. Explanation: An observation is an outlier if it falls more than above the upper quartile or more than below the lower quartile. The minimum value is so there are no outliers in the low end of the distribution.

Real people don’t use the term “outliers.” Instead they say things like: An outlier is defined as ‘having different underlying behavior than the rest of the data’. This is really useless because unless you are doing simulations, you don’t know the underlying behavior, i.e. the distribution, of any one data point.

Given mu and sigma, a simple way to identify outliers is to compute a z-score for every xi, which is defined as the number of standard deviations away xi is from the mean […] Data values that have a z-score sigma greater than a threshold, for example, of three, are declared to be outliers.

SYNONYMS FOR outlier 2 nonconformist, maverick; original, eccentric, bohemian; dissident, dissenter, iconoclast, heretic; outsider.

Outlier = legitimate data point that’s far away from the mean or median in a distribution. While anomaly is a generally accepted term, other synonyms, such as outliers are often used in different application domains. In particular, anomalies and outliers are often used interchangeably.

A value that “lies outside” (is much smaller or larger than) most of the other values in a set of data. For example in the scores 25,29,3,32,85,33,27,28 both 3 and 85 are “outliers”.

The three different types of outliers

• Type 1: Global outliers (also called “point anomalies”):
• Type 2: Contextual (conditional) outliers:
• Type 3: Collective outliers:
• Global anomaly: A spike in number of bounces of a homepage is visible as the anomalous values are clearly outside the normal global range.

A Quick Guide to the Different Types of Outliers

• Type 1: Global Outliers (aka Point Anomalies)
• Type 2: Contextual Outliers (aka Conditional Anomalies)
• Type 3: Collective Outliers.