You probably mean the confidence interval.
When you construct a confidence interval it has a percentage coverage that is based on assumptions about the population distribution. If the population distribution is skewed there is reason to believe that (a) the statistics upon which the interval are based (namely the mean and standard deviation) might well be biased, and (b) the confidence interval will not accurately cover the population value as accurately or symmetrically as expected.
A skewed distribution typically has one tail that is longer or fatter than the other. In a right-skewed distribution, the tail on the right side is longer, while in a left-skewed distribution, the left tail is longer. Therefore, a skewed distribution has one dominant tail, but it can be characterized by its direction (right or left).
If the population distribution is roughly normal, the sampling distribution should also show a roughly normal distribution regardless of whether it is a large or small sample size. If a population distribution shows skew (in this case skewed right), the Central Limit Theorem states that if the sample size is large enough, the sampling distribution should show little skew and should be roughly normal. However, if the sampling distribution is too small, the sampling distribution will likely also show skew and will not be normal. Although it is difficult to say for sure "how big must a sample size be to eliminate any population skew", the 15/40 rule gives a good idea of whether a sample size is big enough. If the population is skewed and you have fewer that 15 samples, you will likely also have a skewed sampling distribution. If the population is skewed and you have more that 40 samples, your sampling distribution will likely be roughly normal.
No, a distribution is considered negatively skewed if the left tail is longer or fatter than the right tail. In this case, the bulk of the data is concentrated on the right side, with a longer tail extending to the left. A positively skewed distribution, on the other hand, has a longer right tail.
Positively skewed.
To determine if the data in a line plot is skewed left, right, or not skewed, you would need to observe the distribution of the data points. If the tail on the left side is longer or fatter, it is left-skewed; if the tail on the right side is longer or fatter, it is right-skewed. If the data points are evenly distributed around a central value, it is not skewed. Without seeing the actual plot, I can't provide a definitive answer.
No, as you said it is right skewed.
A skewed distribution typically has one tail that is longer or fatter than the other. In a right-skewed distribution, the tail on the right side is longer, while in a left-skewed distribution, the left tail is longer. Therefore, a skewed distribution has one dominant tail, but it can be characterized by its direction (right or left).
If the population distribution is roughly normal, the sampling distribution should also show a roughly normal distribution regardless of whether it is a large or small sample size. If a population distribution shows skew (in this case skewed right), the Central Limit Theorem states that if the sample size is large enough, the sampling distribution should show little skew and should be roughly normal. However, if the sampling distribution is too small, the sampling distribution will likely also show skew and will not be normal. Although it is difficult to say for sure "how big must a sample size be to eliminate any population skew", the 15/40 rule gives a good idea of whether a sample size is big enough. If the population is skewed and you have fewer that 15 samples, you will likely also have a skewed sampling distribution. If the population is skewed and you have more that 40 samples, your sampling distribution will likely be roughly normal.
A distribution or set of observations is said to be skewed right or positively skewed if it has a longer "tail" of numbers on the right. The mass of the distribution is more towards the left of the figure rather than the middle.
Symmetric
A positively skewed or right skewed distribution means that the mean of the data falls to the right of the median. Picturewise, most of the frequency would occur to the left of the graph.
skewed right.
In the majority of Empirical cases the mean will not be equal to the median, so the event is hardly unusual. If the mean is greater, then the distribution is poitivelt skewed (skewed to the right).
When a distribution is skewed to the right, the mean is greater than median.
The distribution is skewed to the right.
No, a distribution is considered negatively skewed if the left tail is longer or fatter than the right tail. In this case, the bulk of the data is concentrated on the right side, with a longer tail extending to the left. A positively skewed distribution, on the other hand, has a longer right tail.
A distribution or set of observations is said to be skewed left or negatively skewed if it has a longer "tail" of numbers on the left. The mass of the distribution is more towards the right of the figure rather than the middle.