"The mean of your sampling distribution is about 73.41 percent, essentially identical to the population mean of your complete data set which is about 73.4 percent."
"'True Rate Me' subreddit is like, you can submit yourself and people that are on this subreddit all day will give you an accurate rating but really they're just [ __ ] mean."
"The extremes inform the mean, but not vice versa."
"Mean is synonymous with the word average. How do we take an average? We take that entire group of data, all those numbers, we add them together and we divide by 13."
"The mean of a binomial distribution is given by n multiplied by P."
"Am I being too mean? No, it's Rachel Bellinger who is wrong."
"I think he's the meanest, scariest guy who ever played in the NFL."
"Jim is just a sarcastic lazy condescending lazy um just mean person."
"The front end just looks really menacing, really mean."
"I can't be mean. It's just not in my blood. I can't be like [ __ ] you."
"You cannot narrow a distribution without moving the mean."
"According to the law of large numbers, as n increases, the mean of the sampling distribution will approximate or get very close to the population mean."
"The height of the curve is taller if the distance is closer to the mean."
"To find the mean of a binomial distribution, all you have to do is take N times P."
"To find the mean or average of a data set, you take the sum of all the data points and divide it by how many numbers there are in the data set."
"The mean being the average, so first let's just get the mean here."
"When you subtract the mean from each participant's score, you end up with a variable that has a mean of zero."
"The mean is the value that we're going to get if we add all the values that we have for household monthly income and then divide by the number of individual values that we have."
"That's how you do a reverse mean: you just multiply the mean by the actual amount of people that it relates to."
"When your data is roughly symmetric, the mean and the median will be pretty close together."
"The mean is going to be somewhere in the center."
"The mean and the median will be close together if the data is well-behaved."
"The normal distribution is entirely ran by two values: the mean smack dab in the middle and the standard deviation."
"The mean of the sample means is actually the population mean."
"The mean is nothing more than the average."
"Her result falls within two standard deviations of the mean."
"To find the mean, add the numbers together and then divide by the number of numbers there are."
"We are 95% certain that the true mean lies between the interval values."
"The Karcher mean is the point that minimizes the sum of squared geodesic distances to all points."
"The mean or the average is going to provide an average value by summing all of the values and then dividing by the number of components."
"Adding a constant to each value of a one variable data set changes the mean but not the standard deviation."
"If you want the mean, then you do n multiplied by p."
"So to work out the mean, it's the sum which we worked out as 144, over the total, and it says there's eight girls, so we do 144 divided by eight, which equals eighteen."
"We should be more sure about this being the true population mean than we were in the initial example."
"The expected value of a random variable and the mean are the same thing."
"The mean of uniform probability distribution is (a+b)/2 and variance is (b-a) squared over 12."
"The mean of the distribution is H over H plus T."
"95% of the intervals formed in this manner will contain the true mean."
"The parameter \(\mu\) denotes the expected value or the mean of the random variable."
"The mean of this new Gaussian random variable \(X\) is \(\sum a_i\mu_i\), and the variance is \(\sum a_i^2\sigma_i^2 + \sum_{i\neq j} a_ia_j\sigma_{ij}\)."
"Standard deviation shows variation about the mean."
"The mean and the median fall at the same point when you're perfectly symmetrical."
"When you're skewed right, the mean is going to go a little bit to the right."
"The mean μ is given by np and the standard deviation σ is given by the square root of np multiplied by 1 minus p."
"Most of the data tends to be close to the mean, and less of the data tends to be farther away from the mean."