Use MS Word to complete “Questions to be Graded: Exercises 31 and 32” in Statistics for Nursing Research: A Workbook for Evidence-Based Practice. Submit your work in SPSS by copying the output and pasting into the Word document. In addition to the SPSS output, please include explanations of the results where appropriate.

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ADDITIONAL DETAILS 

z-test, t-test, and the alpha level

Introduction

When you’re doing a statistical test, it can be confusing to know which one is right for your data. You might think that z-tests are used more often than t-tests, but that isn’t always true. In this article we’ll discuss the difference between these two types of tests and how they’re used in various situations as well as how alpha levels change depending on which type of test you choose.

z-test or t-test?

The z-test is used when you have two means (the differences between the two scores).

The t-test is used when you have a mean and a proportion.

The t-test is also used to compare two proportions.

alpha level

The alpha level is a measure of the probability of making a Type I error. It’s always between 0 and 1, meaning that it can never be higher than 1 and never lower than 0.

The significance level is what you choose to accept or reject when there are multiple data points available for testing (like in an experiment). The significance level determines how many data points need to be tested before you decide whether they’re significant enough to believe your hypothesis should be rejected or not. This can help prevent false positives from occurring because if your hypothesis wasn’t true but because we had fewer than 10 samples tested out of 100 total samples we would have missed some smaller effects happening on top of our main finding which may have been missed by us had there been more than 10 samples tested instead!

Alpha level for two-tailed t-test

You can also use the alpha level to control the probability of making a type I error. The formula for this is:

When you use a t-test, you must decide on an alpha level before running your test. This is because it controls how big of an effect we expect to see in our data and what size of sample we need to run our tests with (if there are 10 observations per group). If we want to make sure that at least 5% of all possible errors will be made by choosing an alpha value too low; then we would set our observed accuracy as follows:

Alpha level for one-tailed t-test

You can choose your alpha level in a one-tailed t-test by setting the probability of making a Type I error at 0.05. This is the likelihood that you will reject the null hypothesis when it’s actually false, which means your results are different from what would be expected if there weren’t any difference between two groups.

In order to make sure we’re not making an error that could lead us to falsely conclude something isn’t true, we set our alpha level at 0.05 (or 5%). The higher this number gets set above zero, it means we’re less likely to accept an incorrect conclusion—and therefore more likely to accept a true conclusion if one exists!

When choosing a statistical test, you need to understand the difference between a z-test and a t-test. You also need to understand how the alpha level changes depending on the type of test you choose.

When choosing a statistical test, you need to understand the difference between a z-test and a t-test. You also need to understand how the alpha level changes depending on the type of test you choose.

A z-test is one-tailed when it rejects H0 if Z > Zα (or Z < -Zα). In other words, it says that if your sample mean is higher than or equal to zero (the probability of getting this sample means from normal distributions), then we can reject H0 at that alpha level with 95% confidence interval (CI).

A t-test is two-tailed because it rejects H0 only if there’s evidence against its null hypothesis.

Conclusion

It can be confusing when it comes to choosing between these three types of tests. They all have their advantages and disadvantages. The main difference between them is how much data you are using when doing your statistical analysis on those results. So if you need more information about what type of test will work best for your situation, feel free to ask us!