Statistics

What is the central limit theorem in statistics? What can the central limit theorem tell us about the distribution of the sample mean? The central limit theorem states that the mean of a representative sample will be close to the mean of the larger population. Therefore, we can confidently make inferences about a population from a sample or about a sample from a population, and we can compare samples to each other. Let’s explore each of these general applications of inferential statistics with an example.

What is sampling in research methodology? Why is having a representative sample important? Many research and survey projects rely on sampling as a way to learn about a larger population. Researchers aim to select samples that reflect the target population as closely as possible. Keep reading for the definition of a representative sample, why it’s important, and how to collect samples that are representative of the population in question.

What is the null hypothesis in statistics? What does accepting a null hypothesis tell us about the relationship between two variables? When we use inferential statistics to answer a question, we begin with a null hypothesis. A null hypothesis assumes a relationship between two variables that we’ll accept or reject. If the null hypothesis is rejected, we accept the alternative hypothesis, which is the logical inverse of the null hypothesis. Keep reading to learn about the null hypothesis, why we use it, and what accepting/rejecting it can tell us about data.

What are Type I and Type II errors in hypothesis testing? How can you minimize your chances of accepting a wrong hypothesis? Type I and Type II errors both relate to the result of the null hypothesis. A Type I error occurs when the null hypothesis is mistakenly rejected, whereas a Type II error occurs when the null hypothesis is mistakenly accepted. Keep reading to learn about the difference between a Type I and a Type II error, and how to reduce your chances of making both.