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A surgeon is studying how different surgical techniques impact the healing of tendon injuries. He will compare three different suture repairs biomechanically to determine maximum load. He collects data on 90 different tendons from an animal model. Thirty tendons were repaired using the suture techniques. Which is the most appropriate statistical method for analyzing the results of this study?

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Analysis of variance, or simply, ANOVA, is a type of parametric statistical test used to determine if there’s a significant difference between the means or averages of three or more groups.

And significance is normally defined by a p-value of less than 0.05 or 5%.

Now when doing any parametric test, there are three key assumptions that we have to make about the population.

First, the sample population must have been recruited randomly. Choosing names randomly ensures that the people included in the study will have similar characteristics to the target population.

This is important because that ensures that the results of the t-test can be applied to the target population - meaning it has good external validity!

The second assumption is that each individual in the sample was recruited independently from other individuals in the sample. In other words, no individuals influenced whether or not any other individual was included in the study.

For example, if two friends decided to get their blood pressures measured on the same day, and they were both included in the study, these two individuals would not be independent of each other and the second assumption would not be met.

Like random sampling, independent recruitment of individuals is important because it ensures that the sample population approximates the target population.

The third assumption is that the sample size is large enough to approximate the target population, which usually means having more than 20 people.

If it’s impossible to get a large sample size, then the sample population must follow a normal bell-shaped distribution for the characteristic being studied because that’s what we would expect to see in the target population.

Okay, now let’s say there are three medications available for lowering systolic blood pressure, and you want to figure out if any of the medications work differently than the others.

Let’s say that you find 10 people who take Medication A for 6 weeks, and that afterwards the mean systolic blood pressure for the group is 130 mmHg. Then, you find another 10 people who have been taking Medication B - and afterwards their mean systolic blood pressure is 138 mmHg, and finally you find 10 people who have been taking Medication C - and afterwards their systolic blood pressure is 132 mmHg. Now, the next step is to figure out if 130, 138, and 132 are significantly different from one another, and you do that by performing an ANOVA test.

Specifically, we would use a one-way ANOVA, because we’re looking at one independent variable - which is medication type - that has multiple levels or groups, which are Medication A, B, and C.

This is different than a multi-way ANOVA, which looks at two or more independent variables that each have multiple groups.

One-way analysis of variance, or simply, ANOVA, is a type of parametric statistical test used to determine if there's a significant difference between the means or averages of three or more groups.

To perform a one-way ANOVA, you need to have at least two groups of data that you want to compare. You can then calculate the mean, variance, and standard deviation of each group. The one-way ANOVA test uses these values to determine whether there is a significant difference between the means of the groups.

This test is often used in scientific research to compare the means of different groups, such as comparing the effectiveness of different medications or treatments.

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