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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.
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When population distribution is right skewed is the sampling also with right skewed distribution?
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.
When the median lies to the right of the mean the distribution is probably?
Positively skewed.
What does positive skewness signify in normal distribution?
A normal distribution is not skewed. Skewness is a measure of how the distribution has been pulled away from the normal.A feature of a distribution is the extent to which it is symmetric.A perfectly normal curve is symmetric - both sides of the distribution would exactly correspond if the figure was folded across its median point.It is said to be skewed if the distribution is lop-sided.The word, skew, comes from derivations associated with avoiding, running away, turning away from the norm.So skewed to the right, or positively skewed, can be thought of as grabbing the positive end of the bell curve and dragging it to the right, or positive, direction to give it a long tail in the positive direction, with most of the data still concentrated on the left.Then skewed to the left, or negatively skewed, can be thought of as grabbing the negative end of the bell curve and dragging it to the left, or negative, direction to give it a long tail in the negative direction, with most of the data still bunched together on the right.Warning: A number of textbooks are not correct in their use of the term 'skew' in relation to skewed distributions, especially when describing 'skewed to the right' or 'skewed to the left'.
What is coefficient of skewness in a variable concentration?
A measure of skewness is Pearson's Coefficient of Skew. It is defined as: Pearson's Coefficient = 3(mean - median)/ standard deviation The coefficient is positive when the median is less than the mean and in that case the tail of the distribution is skewed to the right (notionally the positive section of a cartesian frame). When the median is more than the mean, the cofficient is negative and the tail of the distribution is skewed in the left direction i.e. it is longer on the left side than on the right.
Is the value of mean equals 35.4 and value of median equals 35 the shape of the curve skewed is?
the shape of the curve skewed is "right"
