by Santosh

(Australia)

If the mean is 25.8 and standard deviation is 4.7, what does that mean?

First please follow these links and read the information there:

Mean Average, What Is Standard Deviation?, Normal Probability Distribution and Standard Deviation Calculator.

The most likely distribution is a normal distribution. If we assume that the distribution is indeed normal, then we can understand a lot from the statistics that you provided. 25.8 is the mean or average of the data. This is a measure of the central tendency of the data. The standard deviation is a measure of the variability of the data around the mean. Plus or minus one standard deviation from the mean would be expected to include 68.3% of all the data points in the distribution. Plus or minus two standard deviations from the mean would be expected to include 96.4% of all the data points in the distribution.

by Hasinkha S. Tadvi

(Jalgaon)

Are the normal probability curve and normal curve the same?**Answer**

Yes they are. Whenever you see the familiar bell shaped curve, then you know that it is the normal probability curve. Another curve that you might see occasionally is the cumulative normal curve which looks like an misshapen S. Learn more about normal curves at Normal Probability Graphs and at Normal Probability Distributions.

by Anonymous

How is the standard error related to the standard deviation?**Answer:**

Dee Reavis

The standard deviation of a sampling distribution is named the standard error.

Let's consider an example: Suppose that you have a large data population. The data in question is the age of students at a popular college. Since you don't have access to all the data, you need to take samples. You calculate the mean for each sample. You don't know the true mean of the entire population, but you can have a good estimate by taking the standard deviation of the sample means. This standard deviation of the means is also called the standard error of the means.

Note: The sampled means would be normally distributed according to the central limit theorem.