"As our sample size becomes small and smaller, we are less certain that it is representative of our entire population."
"The T distribution allows us to use small samples, which is very beneficial."
"Once our sample size becomes sufficiently large, there really is no practical difference between the T distribution and the Z distribution."
"The probability of having a value further from the true mean is greater when the sample size is small."
"Degrees of freedom is just an adjustment to our sample size that gives us slightly more wiggle room in our estimates."
"The t-distribution is used for small sample sizes or unknown population variance."
"The number of people in a trial makes a difference."
"So, about 120 grams that are there. A lot more than they thought, right? A lot more target."
"What's your sample size and how did you count it? That's the most important thing. And in order to get a large sample size, you have to have a one entry, one exit, a stop loss. It's not going to get too much better than that."
"Small sample sizes often produce statistics that are not very useful and that are not indicative of how a player is actually performing."
"The central limit theorem says that as long as the sample size is large enough it is gonna look normal."
"The sample sizes are admittedly pretty small on the dot cards."
"If the sample size is large enough, the sample distribution taken from any population distribution, regardless of its shape, will approximate a normal distribution."
"Regardless of the shape, the sampling distribution will have the shape of a normal distribution when n is sufficiently large."
"In the limit, I'm going to be able to learn very well what my value of beta1 is from my sample."
"The power of a test increases when the sample size increases."
"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."
"That's the fun part about math, you get an idea of what's true but you really need a large sample size to really be able to confirm it."
"The sample mean is a good estimate of the true mean in the sense that it approaches the true mean as your sample size increases."
"SVM seems to work pretty well for in most cases but it's particularly good when we have little training sample because it's a regularized method."
"Within plus or minus one standard deviation, roughly two-thirds of your total sample population is captured."
"As we increase the sample size, the error decreases."
"The standard deviation of the sample means is the population standard deviation divided by the square root of the sample size."
"A four-game sample size shouldn't change everything about a dude's career in their prime."
"Always it is better to calculate the sample size before starting a study, that is at the planning stage of the study."
"Your sample size depends upon the study design and your primary outcome measure."
"You can add 10 percentage to the sample size for each confounder you expect."
"As a researcher, you should know the basic steps of calculating sample size and power."
"Sampling distributions are typically normal, as long as your sample size is big enough."
"We can ignore this assumption of normality as long as our sample size is big enough."
"The larger the number of observations, the smaller the standard errors should be."
"Just a modest increase in sample size can really decrease that standard error."
"The beauty of statistics is that you do not have to increase the sample size to a crazy number to get the benefits of that increase."
"When you did science experiments in high school, you did a sample size; you didn't test everything."
"If we have the power, the significance, and the effect size, we can calculate the sample size."
"The larger the sample, the greater the precision."
"The larger your sample size, the more confident you can be in the significance of your findings."
"We need a sample size of 303 in each group to detect a 14% increase in risk with the power of 80%."
"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."
"Irrespective of the shape of the initial population, the sampling distribution becomes almost normal as the sample size gets large enough."
"As the sample size increases the shape of sampling distribution approaches normal distribution."
"The distribution of the residuals should be nearly normal, and with a large sample size, this is really good."
"G power is a software that can help you determine the required sample size for your experiment."
"The only way that we can reduce the probability of both Type One and Type Two errors together is if we increase the sample size that we use in our analysis."
"Once the sample reaches a reasonable size, it becomes a good approximation of the population standard deviation."
"You always want to choose the smallest sample size you can that will give you an accurate answer because it's more economical."
"The adjusted R square is printed out right here, this adjustment is for sample size and number of predictors."
"Performing our experiment with a larger sample size... would make it more likely that my data would indicate something significant."
"It's important to take a huge sample size of what it is you need to get into medical school and not just trust one person."
"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 Law of large numbers: the more the larger your sample, the more the sample average is like the population average."
"The test has to be repeated to reduce the effects of a small sample size."
"Increasing n can decrease both type of error, that is type 1 and type 2."
"If your sample size is bigger, you will have less variability."
"The central limit theorem tells us that when we have big enough samples, the sampling distribution will be normal."
"The law of large numbers ensures that the larger your sample size, the more accurate of a representation your final result will be."
"The fraction of the population that you've sampled doesn't matter; it's the sample size itself that's important."
"The data is the number of samples by a number of features."