Standard deviation is a characteristic of a random variable describing how dispersed samples from it are, standard error (of mean) is a description of how confident you are in where the variable's mean lies. The more samples you have the smaller your standard error because you can be more confident of where the mean lies, but the underlying deviation is the same. If the standard deviation is larger, then your confidence in where the mean lies will also be broadened and you will need more samples to get to the same standard error. More on reddit.com

Im not sure if Im getting this correctly: How many factors are involved? What exactly are you missing out? More on reddit.com

It’s roughly the average distance from the mean of the different individuals (elements) of the population. So it’s a measure of the variability of the population. A low standard deviation means most if not all the elements are close to the mean. In your example most of the individuals would weigh between 160 and 200 pounds (2 standard deviations). Practically all would be between 150 and 210 (3 standard deviations). More on reddit.com

You are asking the right sort of questions. Please, PLEASE keep doing this. By 'average' it sounds like you're talking about the 'mean average'. There is another average called the 'median', which is the middle value in a group of numbers (or the midpoint between the middle two if there are an even number of data points) The average absolute deviation isn't used much, but the median absolute deviation (MAD) is. It's used to describe the variation of data when some of the data is extremely far from the middle. Statistical methods that handle extreme data well are called "robust". The standard deviation is special, but what's REALLY special is the squared standard deviation, or variance. After the 3rd step, you have the variance. The variance is special because of the way it can be used to describe uncertainty or variation in complicated situations. For example: if you're trying to describe the variation in the data from a single sample, you have a lot of options, including the standard deviation and the MAD. But what if you weren't interested in just the average of one sample, but the average difference between two samples? You can do this by taking the variance (sd squared) of one sample, and the variance of the other, and adding them together. Then you can use that do things like find out if the samples are from different groups or similar ones, which is really useful if you're doing things like testing a new medical treatment. Adding up the variance doesn't work if you're using a method based on absolute values, but it works for squares. 3. It's "standard" because "average" was taken. Standard deviation a measure of the typical deviation from the mean. "Typical" is a vague synonym for "average" in stats, but we couldn't use "average" because it's not a mean or a median. So the next best word was "standard". (Actually, "average deviation" is another term for "average absolute deviation") 4. No, and it hurts a bit that you would say that. If statisticians were trying to make your lives harder on purpose, they would have kept the link between smoking and lung cancer a secret. More on reddit.com