Lesson 24, Exercises 1—5 The following helpful tips are numbered to correspond with the exercise number to which they refer: Type the t and p values out underneath your copied and pasted output. Error Difference F Sig. Instead of identifying these values on your output, as the text states, please write them into your Microsoft Word file as written answers for 2 a, b, and c.
This tutorial assumes that you have: In this example, we will determine if the mean number of older siblings that the PSY students have is greater than 1. We will follow our customary steps: Write the null and alternative hypotheses first: Determine if this is a one-tailed or a two-tailed test.
Because the hypothesis involves the phrase "greater than", this must be a one tailed test. The variable of interest, older, is on a ratio scale, so a z-score test or a t-test might be appropriate. Because the population standard deviation is not known, the z-test would be inappropriate.
We will use the t-test instead. Calculate the t value, or let SPSS do it for you! The command for a one sample t tests is found at Analyze Compare Means One-Sample T Test this is shorthand for clicking on the Analyze menu item at the top of the window, and then clicking on Compare Means from the drop down menu, and One-Sample T Test from the pop up menu.
The One-Sample t Test dialog box will appear: Select the dependent variable s that you want to test by clicking on it in the left hand pane of the One-Sample t Test dialog box.
Then click on the arrow button to move the variable into the Test Variable s pane. In this example, move the Older variable number of older siblings into the Test Variables box: Click in the Test Value box and enter the value that you will compare to.
In this example, we are comparing if the number of older siblings is greater than 1, so we should enter 1 into the Test Value box: Click on the OK button to perform the one-sample t test. The output viewer will appear. There are two parts to the output. The first part gives descriptive statistics for the variables that you moved into the Test Variable s box on the One-Sample t Test dialog box.
In this example, we get descriptive statistics for the Older variable: This output tells us that we have 46 observations Nthe mean number of older siblings is 1.
The standard error of the mean the standard deviation of the sampling distribution of means is 0. The second part of the output gives the value of the statistical test:The basic null hypothesis is that the population mean difference is equal to a hypothesized value, The differences are mutually independent.
4. The differences all have the same median. Specify the direction of the alternative hypothesis. This direction applies to t-test, power report, and Wilcoxon. In the example above, we use a t test for independent means to try and disprove the Null Hypothesis. To determine the value needed to reject the Null Hypothesis, we need to refer to a table (see below).
The independent-samples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are Null Hypothesis: H 0: m 1 = m 2 where m It is common to use this test in conjunction with other measures such as an examination of skewness, kurtosis.
Hypothesis Test for Difference of Means. And our alternative hypothesis, I'll write over here. It's just that it actually does do something. So let's think about this.
So we have the null hypothesis. Let me draw a distribution over here. The null hypothesis says that the mean of the differences of the sampling distributions should be. Returning to the two-sample t-test, the steps to conduct the test are similar to those of the one-sample test.
Establish Hypotheses. The first step to examining this question is to establish the specific hypotheses we wish to examine. Specifically, we want to establish a null hypothesis and an alternative hypothesis to be evaluated with data.
test an F-test, similar to the t-test). Again, there is no reason to be scared of this new test or distribution.
This is a test of the null that none of the independent variables have predictive power. We could use another null such as H 0: With this null hypothesis, we can write the test statistic as .