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Answer: Asymmetrical. In positively-skewed distributions, the mean is to the right of the peak
Skewness refers to asymmetry in a statistical distribution. It can be quantified to define the extent to which a distribution differs from a normal distribution. **Key points:** - A left-skewed distribution (negatively-skewed) has a long left tail, and the mean is to the left of the peak. - A right-skewed distribution (positively-skewed) has a long right tail, and the mean is to the right of the peak. Option C correctly states that skewness refers to asymmetry and that in positively-skewed distributions, the mean is to the right of the peak. **Why other options are incorrect:** - **Option A**: Incorrect because skewness refers to asymmetry, not symmetry. - **Option B**: Incorrect because in negatively-skewed distributions, the mean is to the left of the peak, not to the right. - **Option D**: Incorrect because in left-skewed distributions, the mean does not coincide with the peak; it is to the left of the peak.
Author: Nikitesh Somanthe
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Which of the following statements is most accurate? Skewness refers to the extent a distribution is:
A
Symmetrical. In negatively-skewed distributions, the mean is to the left of the peak
B
Asymmetrical. In negatively-skewed distributions, the mean is to the right of the peak
C
Asymmetrical. In positively-skewed distributions, the mean is to the right of the peak
D
Asymmetrical. In left-skewed distribution, the mean coincides with the peak
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