A residual is simply equal to the predicted value minus the actual value. The line of best fit is found by minimizing the squared distances between the points and the line of best fit - this is known as minimizing the sum of squared residuals. In simpler terms, it involves finding the ‘line of best fit’ that represents two or more variables. Linear Regression is one of the most fundamental algorithms used to model relationships between a dependent variable and one or more independent variables. ![]() ![]() You can see the image below as a reference to guide which test you should use: You would use a t-test when you don’t know the population variance and have a small sample size. A z-test is used when you know the population variance or if you don’t know the population variance but have a large sample size.Ī T-test is a hypothesis test with a t-distribution that uses a t-statistic. Understanding the differences between z-tests and t-tests as well as how and when you should choose to use each of them, is invaluable in statistics.Ī Z-test is a hypothesis test with a normal distribution that uses a z-statistic. For example, you could try to prove when rolling a dye that one number was more likely to come up than the rest. Hypothesis testing is the basis of any research question and often comes down to trying to prove something did not happen by chance. Confidence intervals are often very important in medical research to provide researchers with a stronger basis for their estimations.Ī confidence interval can be shown as “10 +/- 0.5” or to give an example. The confidence interval suggests a range of values for an unknown parameter and is then associated with a confidence level that the true parameter is within the suggested range of. So if the initial definition doesn’t stick with you, remember the example I just gave above!Ģ) Confidence Intervals and Hypothesis TestingĬonfidence intervals and hypothesis testing share a very close relationship. Similarly, a p-value of 0.05 is the same as saying, “5% of the time, we would see this by chance.” In practice, if the p-value is less than the alpha, say of 0.05, then we’re saying that there’s a probability of less than 5% that the result could have happened by chance. The most technical and precise definition of a p-value is that it is the probability of achieving a result that’s just as extreme or more extreme than the result if the null hypothesis is too. In this article, I’m going to go over these 10 concepts, what they’re all about, and why they’re so important.
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