Assertion: sample's distribution | /r/HasProperty | skewed
Facets:
value: for large sample sizes
type: PURPOSE
value: to the right
type: LOCATION
value: despite the fact
type: OTHER
value: as fewer songs
type: OTHER
value: arbitrarily
type: MANNER
value: tremendously
type: DEGREE
Source sentences:
text: For tremendously skewed distributions (e.g., the distribution of lottery payouts), a much larger sample will be required before the distribution of sample means is approximately normal.
text: So if the original distribution is right-skewed, the sampling distribution would be right-skewed; and if the original distribution is left-skewed, then the sampling distribution will also be left-skewed.
text: The distribution is skewed right, as seen above.
text: The distribution here is right skewed as fewer songs as the number of minutes increases.
text: a) the distribution is skewed right
text: For example, if the distribution is skewed right (positively skewed), the mean will increase to account for the few larger observations that pull the distribution to the right.
text: A well-known class of distributions that can be arbitrarily skewed is given by the log-normal distribution.
text: The distribution is said to be left-skewed, left-tailed, or skewed to the left, despite the fact that the curve itself appears to be skewed or leaning to the right; left instead refers to the left tail being drawn out and, often,
text: The distribution is said to be right-skewed, right-tailed, or skewed to the right, despite the fact that the curve itself appears to be skewed or leaning to the left; right instead refers to the right tail being drawn out and, often, the mean being skewed to the right of a typical center of the data.
text: So if the original distribution is right-skewed, the sampling distribution would be right-skewed; and if the original distribution is left-skewed, then the sampling distribution will also be left-skewed.
text: The sampling distribution will also be right-skewed for large sample sizes.