In the normal distribution, the mean and median coincide,
and 50% of the data are below the mean.
A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.
Assuming that we have a Normal Distribution of Data, approx. 65% of the data will fall within One Sigma.
It depends on the shape of the distribution. For standard normal distribution, a two tailed range would be from -1.15 sd to + 1.15 sd.
34.1% of the data values fall between (mean-1sd) and the mean.
95% is within 2 standard deviations of the mean.
A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.
2.275 %
The answer will depend on what the distribution is. Non-statisticians often assum that the variable that they are interested in follows the Standard Normal distribution. This assumption must be justified. If that is the case then the answer is 81.9%
we prefer normal distribution over other distribution in statistics because most of the data around us is continuous. So, for continuous data normal distribution is used.
Assuming that we have a Normal Distribution of Data, approx. 65% of the data will fall within One Sigma.
z = ±0.44
It depends on the shape of the distribution. For standard normal distribution, a two tailed range would be from -1.15 sd to + 1.15 sd.
34.1% of the data values fall between (mean-1sd) and the mean.
You can use a normal distribution to approximate a binomial distribution if conditions are met such as n*p and n*q is > or = to 5 & n >30.
95% is within 2 standard deviations of the mean.
It is 84.3%
Yes, If you have a large data set, you can approximate the discrete data by Normal distribution (which is continuous). An example would be, "A coin is tossed 1000 times. What is the probability of rolling between 300 and 400 heads?" This problem, usually solved by Binomial distribution (which is a discrete distribution), is very difficult to solve because of the large data set and can be approximated by the Normal distribution.