"The Galton board illustrates one of the most prominent distributions in all of probability, known as the Normal distribution, more colloquially known as a Bell curve."
"Everything we do in inferential statistics is to some degree, based on the normal distribution."
"What's interesting about the normal distribution is that it shows up in nature all the time."
"It's nice to see a fairly defined bell curve."
"The central limit theorem states that more samples lead to a distribution approximating normal."
"These residuals should be normally distributed around this line of best fit."
"The central limit theorem says that as long as the sample size is large enough it is gonna look normal."
"The normal probability plot is used to determine whether the y data fits a normal distribution."
"The basic idea behind the Central Limit Theorem is this: let's say we collect samples of size n from a population and then we calculate the mean of each of those samples and then plot those means on a histogram, the histogram will approximate a normal distribution."
"Many datasets are Gaussian; many datasets follow a normal distribution which is represented by a Gaussian."
"The height of the curve is taller if the distance is closer to the mean."
"The mean, median, and mode of a normal distribution are equal."
"For these types of distributions, roughly 68 percent of your data should fall between one standard deviation of the mean plus or minus one standard deviation."
"I cannot stress enough how important the normal distribution is to statistics."
"The normal probability distribution pops up everywhere; it's kind of ingrained into the fabric of the world we live in."
"The normal distribution... it's the one that's so common everywhere."
"If the population is not normal but the sample size is greater than 30, then the sampling distribution of sample means approximates a normal distribution."
"In a normal distribution, mean, median, and mode all lie at the center."
"A normal distribution curve... one standard deviation either direction of the mean explains about 68% of the results."
"The beautiful thing about the Gaussian distribution is it appears many times in the world."
"Most importantly, conditionally gives you a Gaussian distribution."
"Normal distribution is a continuous probability density that has a probability density function which gives us a symmetrical bell curve."
"The standard normal distribution is a type of normal distribution that has a mean of zero and a standard deviation of one."
"We're pretending that the sample mean has a normal distribution. That's something we're justified to do, by the central limit theorem."
"68% of the values in that curve will fall within one standard deviation."
"68% of the data will be within one standard deviation of the mean."
"The central limit theorem states that the distribution of many sample means will be normally distributed, even if the underlying data being sampled from is not normally distributed."
"Gauss rank is a transformation which transforms any arbitrary distribution to a Gaussian normal distribution."
"The normal distribution even allows us to work backwards to solve some really cool problems."
"Now we move on to the normal distribution, which is perhaps the most important distribution."
"A normal distribution is denoted using these symbols, so X is the random variable, N stands for the fact that this is a normal distribution."
"A normal distribution will have skewness of 0, that means that the distribution is completely symmetric."
"The central limit theorem says, as long as your samples are 30 or larger, even if the population is unknown or not normal, the sampling distribution will still be normal."
"The central limit theorem says the means drawn from multiple samples of the data when plotted will resemble a bell-shaped normal curve."
"If it says almost the same number for Mean, Median, and Mode, I automatically know it's a normal distribution."
"The empirical rule helps establish intervals that apply to normally distributed data."
"The central limit theorem states that the distribution of sample means in a population will constitute a normal distribution even if the population itself is not normally distributed."
"Her result falls within two standard deviations of the mean."
"Whenever you have a phenomenon which is noisy, and the noise that you observe is created by adding lots of little pieces of randomness that are independent of each other, the overall effect that you're going to observe can be described by a normal random variable."
"The residual terms must be normally distributed, not the dependent or independent variables."
"We're going to work with the normal distribution, which is the typical Gaussian bell curve."
"Sampling distributions are typically normal, as long as your sample size is big enough."
"The normal model is probably one of the most used models in all of statistics."
"If your data follows a normal model, it becomes really easy to use."
"The normal distribution... the mean, the median, and the mode are the same number."
"The normal distribution is neither too peaked nor too flat-topped."
"Normal data is data that follows a common pattern in nature called the bell curve."
"The 68- 95-99.7% rule... tells us how likely it is that a normal random variable will be a certain distance from its mean measured in terms of standard deviation."
"The chance that x is within 2 standard deviations of its mean is about 95%."
"The shape of this distribution will be normal, meaning Gaussian bell-shaped."
"The central limit theorem tells us that when the sample size is large enough, the sampling distribution of the sample mean will be approximately normally distributed, no matter what the population distribution looks like."
"Because it's continuous, I can't necessarily make this perfect model, but because I know it's normal, I could put all the options on this continuous x-axis."
"Irrespective of the shape of the initial population, the sampling distribution becomes almost normal as the sample size gets large enough."
"The total area under the standard normal curve is one."
"Normal distributions are probably one of the most important topics."
"Every normal curve is completely described by its mean and standard deviation."
"Normal distributions are good descriptions for some distributions of real data."
"Many statistical inference procedures are based on the normal curve."
"The normal distribution is characterized by a symmetric bell-shaped curve where the mean and median value are both at the exact center of the distribution."
"The normal distribution is perhaps the most important distribution in all of statistics."
"Many real-world phenomena like IQ test scores and human heights follow a normal distribution."
"The arithmetic Brownian motion dynamics implies that the process follows a normal distribution."
"The central limit theorem says even if you have a non-normal population, a sampling distribution will always be normal, provided that your sample size is greater than or equal to 30."
"The bell curve represents a lot of things we see in real life, for example, heights."
"The central limit theorem tells us that when we have big enough samples, the sampling distribution will be normal."
"This probability represents an area because Y follows a normal distribution."
"The normal distribution can be described, interestingly enough, with just two numbers: the mean and the standard deviation."
"In a standardized normal distribution, the mean becomes 0, the area under the curve becomes 1, and sigma becomes 1."
"Plus or minus one sigma spans 68.3 percent of the area."
"Plus or minus two sigma spans 95.4 percent of the area."
"Plus or minus three sigma spans 99.7 percent of the area."
"The Gaussian distribution is the most usual distribution that you will encounter in practice."
"A lot of populations follow what's called a normal distribution, which is a distribution that looks like a bell curve."