What Is a Null Hypothesis in Statistics?

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

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

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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.

Real-Life Applications of Probability

Real-Life Applications of Probability

What exactly is probability? How can mathematical probability help us in real life? Probability is a mathematical ratio that communicates the likelihood of a particular event over all other possible outcomes. It allows us to manage uncertainty by measuring risks and putting possible outcomes in perspective. In this article, we’ll discuss some real-life applications of probability.

Inferential Statistics: Examples in Real life

Inferential Statistics: Examples in Real life

What are inferential statistics? How are inferential statistics applied in real life? Inferential statistics are a powerful research tool due to a statistics tenet called the central limit theorem. 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. In this article, we’ll explore inferential statistics examples in real life.

Seth Stephens-Davidowitz: Why Big Data Matters

Seth Stephens-Davidowitz: Why Big Data Matters

What is big data? Why does Seth Stephens-Davidowitz care about it? In Everybody Lies, Seth Stephens-Davidowitz says information from big data can be used for the greater good. But to do so, data researchers have to understand big data’s inherent strengths—and avoid its inherent weaknesses. Learn more about big data, Seth Stephens-Davidowitz’s definition of it, and why it’s important for research.

Invisible Women: Data Bias Works in Men’s Favor

Invisible Women: Data Bias Works in Men’s Favor

How does Invisible Women define data bias? How does data bias hurt women on a healthcare and safety level? As explained in Invisible Women, data bias occurs when socio-cultural prejudices affect systematic processes. The author, Caroline Criado Perez, specifically talks about how a lack of information about the female experience affects women’s health, safety, and economic standing. Continue reading to learn more about gender data bias.

The 4 Benefits of Big Data, Explained In Detail

The 4 Benefits of Big Data, Explained In Detail

What are the benefits of big data? Do you want to know how to use data well? Despite all of the potential advantages, Seth Stephens-Davidowitz acknowledges that it’s easy to use big data ineffectively. To get the most out of big data, Stephens-Davidowitz says you should focus on its four main benefits: new types of information, unprecedented honesty, high resolution, and easy cause-effect analysis. Keep reading for the four benefits of big data that are explained in Everybody Lies.