skewed right skewed left

Front

What does it mean for a distribution to be skewed right?

Back

A distribution is skewed right (positively skewed) when the tail on the right side is longer or fatter than the left side, indicating that the mean is greater than the median.

Front

What does it mean for a distribution to be skewed left?

Back

A distribution is skewed left (negatively skewed) when the tail on the left side is longer or fatter than the right side, indicating that the mean is less than the median.

Front

What is the relationship between mean and median in a right-skewed distribution?

Back

In a right-skewed distribution, the mean is greater than the median.

Front

What is the relationship between mean and median in a left-skewed distribution?

Back

In a left-skewed distribution, the mean is less than the median.

Front

What does a symmetrical distribution look like?

Back

A symmetrical distribution has equal tails on both sides, meaning the mean and median are equal.

Front

How can you identify a skewed distribution from a histogram?

Back

In a histogram, a right-skewed distribution will have a longer tail on the right, while a left-skewed distribution will have a longer tail on the left.

Front

What is the impact of outliers on skewness?

Back

Outliers can significantly affect the skewness of a distribution; a few high outliers can create a right skew, while a few low outliers can create a left skew.

Front

What is the mode in relation to skewed distributions?

Back

In a right-skewed distribution, the mode is less than the median, which is less than the mean. In a left-skewed distribution, the mode is greater than the median, which is greater than the mean.

Front

What is the definition of skewness in statistics?

Back

Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable.

Front

How does the shape of data affect statistical analysis?

Back

The shape of data can influence the choice of statistical methods; for example, parametric tests assume normality, while non-parametric tests do not.