Hypothesis tests are used to test the validity of a claim that is made about a population. This claim that’s on trial, in essence, is called the null hypothesis (H0). The alternative hypothesis (Ha) is the one you would believe if the null hypothesis is concluded to be untrue. Learning how to find the p-value in statistics is a fundamental skill in testing, helping you weigh the evidence against the null hypothesis.

In statistics, r value correlation means correlation coefficient, which is the statistical measure of the strength of a linear relationship between two variables. If that sounds complicated, don't worry — it really isn't, and I will explain it farther down in this article. But before we get into r values, there's some background information you should understand first.

Statistics involves a lot of data analysis, and analysis is built with math notation and formulas — but never fear, your cheat sheet is here to help you organize, understand, and remember the notation and formulas so that when it comes to putting them into practice or to the test, you’re ahead of the game!10 tips for the statistically savvy sleuthHere are some tips on what to do when you encounter statistics in the real world or in the workplace so you can avoid some of the most common statistical mistakes out there.

The t-table (for the t-distribution) is different from the z-table (for the z-distribution). Make sure you understand the values in the first and last rows. Finding probabilities for various t-distributions, using the t-table, is a valuable statistics skill. How to use the t-table to find right-tail probabilities and p-values for hypothesis tests involving t: First, find the t-value for which you want the right-tail probability (call it t), and find the sample size (for example, n).

If you know the standard deviation for a population, then you can calculate a confidence interval (CI) for the mean, or average, of that population. When a statistical characteristic that’s being measured (such as income, IQ, price, height, quantity, or weight) is numerical, most people want to estimate the mean (average) value for the population.

You can find a confidence interval (CI) for the difference between the means, or averages, of two population samples, even if the population standard deviations are unknown and/or the sample sizes are small. The goal of many statistical surveys and studies is to compare two populations, such as men versus women, low versus high income families, and Republicans versus Democrats.

You can calculate a confidence interval (CI) for the mean, or average, of a population even if the standard deviation is unknown or the sample size is small. When a statistical characteristic that’s being measured (such as income, IQ, price, height, quantity, or weight) is numerical, most people want to estimate the mean (average) value for the population.