Statistics Archives

What Does “Statistically Significant” Mean?

What is significance in statistics? What does “statistically significant” mean? In statistics, significance refers to the degree of confidence or certainty in the results of a statistical analysis being attributable to a specific cause. When results are said to be “statistically significant,” the observed relationship between variables is unlikely to be due to chance. Keep reading to learn about the concept of statistical significance, explained in simple terms.

The Doppelganger Effect in Big Data, Explained

What is the doppelganger effect in data? How is the method used to study people? A big data technique Seth Stephens-Davidowitz identifies is the doppelganger method. It’s a technique where researchers make predictions about one person by studying another person who’s statistically similar to the first person. Learn more about the power of doppelgangers, as explained in Everybody Lies.

The Importance of Reliability in Data Collection

Why is reliability important in data collection? What are the main challenges inherent in collecting reliable data? As we use statistical data to inform our lives and society, we need them to be both accurate and precise. Therefore, collecting quality data is the true challenge and art of producing reliable, constructive statistics. Keep reading to learn about the importance of reliability in data collection.

The Central Limit Theorem: Statistics Applied

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.

Everybody Lies: Quotes About Data Science

What are the most memorable quotes from Everybody Lies? How can these quotes help you understand data science? Everybody Lies, by Seth Stephens-Davidowitz, is about big data’s potential to revolutionize social science research. The book’s central premise is that people reveal more about themselves when making web searches than they would ever reveal in public or in a traditional survey. Read more for a few Everybody Lies quotes to explain Stephens-Davidowitz’s argument.

Inferential Statistics 101: Hypothesis Testing

What is hypothesis testing? How do you know if a hypothesis is true? Hypothesis testing is an inferential statistical method by which we determine whether our tentative assumptions are true. Based on our statistical analyses, we can either accept these hypotheses as true or reject them as false with varying degrees of certainty. Let’s look at the common conventions around inferential statistics and hypothesis testing.

Representative Sample: Definition and Methods

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 a Null Hypothesis in Statistics?

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.

Everybody Lies: Book Overview and Takeaways

What is the book Everybody Lies about? What should you take away from the book? In Everybody Lies, Seth Stephens-Davidowitz argues that people willingly confess all of their secrets in their Google searches and other web activity. This information can be found through big data and can be used for the greater good. Read below for a brief overview of the book Everybody Lies by Seth Stephens-Davidowitz.

Type I and Type II Errors in Hypothesis Testing

The Millionaire Next Door Formula for Net Worth

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.