Statistics How To

Probability in Real Life > Omaze Odds of Winning Omaze Odds of Winning: How to Calculate Probabilities Back in January of 2019, I won an Omaze contest and took a surfing trip to Hawaii. Omaze offers some incredible opportunities, but the odds of winning are generally very small. Your probability of winning increases if you … Read more

Descriptive Statistics > Stemplot / Stem and Leaf Plot What is a Stemplot? A stemplot is like a histogram — they are both tools to help you visualize a data set. Stemplots show a little more information than a histogram and have been a common tool for displaying data sets since the 1970s. They are … Read more

Intro to Statistics > You may want to read Part 1 and 2 of Intro to Statistics first. When we perform a survey or experiment, we sometimes want to find out an interval where we can expect to find the majority (95%) of results; the interval that contains 95% of results is called the 95 … Read more

Intro to Statistics > Binomial Proportions Previous: Statistical Assumptions Analyzing the Difference Between Two Groups Using Binomial Proportions So far in Intro to Statistics, we’ve covered many essential foundations. Let’s put them into action while looking at another common type of analysis involving binomial proportions. We are surveying the population in Flowing Wells, but we’ll … Read more

Intro to Statistics > Part 6: The Central Hypothesis Previous: Statistics Case Studies What is the Central Hypothesis?   The central hypothesis is another name for the Null Hypothesis. It’s central, not because you think or hope it’s true. Usually, it’s the exact opposite. For example, a pharmaceutical company testing a new drug obviously wants … Read more

Intro to Statistics > Statistics Case Studies Previous: Type I & Type II Errors. The following six short statistics case studies explore Type I Error and Type II Error under various circumstances. The odd numbered cases concentrate on Type I Error. These cases illustrate that the expected frequency of Type I Error does not change … Read more

Intro To Statistics > Binomial Approximation Previous: The Limited Meaning of Statistical Significance Binomial Approximation with the Z-Distribution The z-distribution is important because it is the ultimate source of many of the formulas used in statistics. The sampling distributions for binomial variables are discrete distributions (discrete values such as 0, .01, .02, …, 1 on … Read more

Intro to Statistics > Bayesian Analysis Previous: Frequentist Statistics Bayesian analysis provides a special method for calculating probability estimates, for choosing between hypotheses, and for learning about population statistic values. To explore its basic workings, we’ll start with a scenario involving medical testing and diagnosis. Basics of Bayesian Analysis Table 12.1 shows the various possible … Read more

Statistics Definitions > What is Statistical Significance? Statistical Significance is a way to tell you if your test results are solid. Statistics isn’t an exact science. In fact, you can think of stats as very finely tuned guesswork. As stats is guesswork, we need to know how close our “guess” is. That’s where significance comes … Read more

Hypothesis Testing > False Discovery Rate Contents: What is the False Discovery Rate? FDR Formula FDR in hypothesis testing FDR in medical testing Adjusting the false discovery rate Case Example What is the False Discovery Rate? The false discovery rate (FDR) is the expected proportion of type I errors. A type I error is where … Read more