Central Limit Theorem: A sampling distribution of the mean is approximately normally distributed if the sample size is sufficiently large. This is true no matter what the population distribution is.

Conventional statistical methods often test for group differences in a single parameter of a distribution, usually the conditional mean (for example, differences in mean body mass index (BMI; kg m −2) ...

We have previously discussed the importance of estimating uncertainty in our measurements and incorporating it into data analysis 1. To know the extent to which we can generalize our observations, we ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Describe the abstract idea of a sampling distribution and how it reflects the sample to sample variability of a sample statistic or point estimate. Identify the ...

In Hypothesis Testing 1, you were introduced to the ideas of hypothesis testing in the context of deciding whether a coin was fair or biased in favor of heads. In this section hypothesis testing ...

We study the asymptotic covariance function of the sample mean and quantile, and derive a new and surprising characterization of the normal distribution: the asymptotic covariance between the sample ...

The normal distribution (also known as the Gaussian distribution) is arguably the most important distribution in Statistics. It is often used to represent continuous random variables occurring in ...

When particles in a sample are the same size, one particle can be measured to report the result. If the sample has a narrow distribution, such as 10-25 µm, then measurement of just a few particles can ...