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What should the standard deviation be for a normal distribution?
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The standard normal distribution is a normal distribution with a mean of zero and standard deviation of 1.
Is standard deviation always 68?
For an approximately normal data set, the values within one standard deviation of the mean account for about 68% of the set; while within two standard deviations account for about 95%; and within three standard deviations account for about 99.7%.
Can I use mean for non normal data?
The central tendency of your data set (Mean) is especially very sensitive to outliers and may result in a Non-Normal distribution. Not every outlier is caused by a special reason. Extreme values should be removed the data only if there are more of them than expected under normal conditions.
Does standard deviation make sense for non normal distribution?
Normal distribution’s characteristic function is defined by just two moments: mean and the variance (or standard deviation). Therefore, for normal distribution the standard deviation is especially important, it’s 50% of its definition in a way.
What does standard deviation tell us in non-normal distribution?
In a normal distribution, the 68-95-99.7 rule imparts standard deviation a lot of meaning, but what would standard deviation mean in a non-normal distribution (multimodal or skewed)? Would all data values still fall within 3 standard deviations?
How are data transformed into the normal distribution?
Data from any normal distribution may be transformed into data following the standard normal distribution by subtracting the mean and dividing by the standard deviation .
Is the density curve of a normal distribution symmetrical?
The Normal Distribution. A normal distribution has a bell-shaped density curve described by its mean and standard deviation . The density curve is symmetrical, centered about its mean, with its spread determined by its standard deviation. The Standard Normal curve, shown here, has mean 0 and standard deviation 1.
Is the calculated mean wrong for non-normally distributed data?
However if the samples are sufficiently large the Central Limit Theorem which guarantees approximate normal distribution for the mean can be applied for the adoption of parametric methods. The calculated mean and the standard deviation are not wrong for non-normal distributed data, nor do they lead to wrong results, as you wrote.