Where to find p value in spss

In this example, the value of the 11 AM section is So you would type 11 in the Group 2 text box: Click on the Continue button to close the Define Groups dialog box. The output viewer will appear with the results of the t test. The results have two main parts: descriptive statistics and inferential statistics.

First, the descriptive statistics: This gives the descriptive statistics for each of the two groups as defined by the grouping variable. In this example, there are 14 people in the 10 AM section N , and they have, on average, 0. There are 32 people in the 11 AM section N , and they have, on average, 1. The last column gives the standard error of the mean for each of the two groups. The second part of the output gives the inferential statistics: The columns labeled "Levene's Test for Equality of Variances" tell us whether an assumption of the t-test has been met.

The t-test assumes that the variability of each group is approximately equal. If that assumption isn't met, then a special form of the t-test should be used. Look at the column labeled "Sig.

In this example, the significance p value of Levene's test is. The column labeled "t" gives the observed or calculate t value. In this example, assuming equal variances, the t value is 1. We can ignore the sign of t for a two tailed t-test. The column labeled "df" gives the degrees of freedom associated with the t test.

In this example, there are 44 degrees of freedom. The column labeled "Sig. In this example, the p value is. If this had been a one-tailed test, we would need to look up the critical t in a table. In this example,. That implies that we failed to observe a difference in the number of older siblings between the two sections of this class. If there already is a variable in the Grouping Variable box, click on it if it is not already highlighted, and then click on the lower arrow which should be pointing to the left.

Then type the value that splits the variable into two groups. Group one is defined as all scores that are greater than or equal to the cut point. There's no full consensus among data analysts which approach is better. I personally always report 2-tailed p-values whenever available. A major reason is that when some test only yields a 1-tailed p-value, this often includes effects in different directions. We compared young to middle aged people on a grammar test using a t-test. Let's say young people did better.

This resulted in a 1-tailed significance of 0. This p-value does not include the opposite effect of the same magnitude: middle aged people doing better by the same number of points. The figure below illustrates these scenarios. Young people performed best, old people performed worst and middle aged people are exactly in between. Now this p-value does include the opposite effect of the same magnitude.

Now, if p for ANOVA always includes effects in different directions, then why would you not include these when reporting a t-test? In fact, the independent samples t-test is technically a special case of ANOVA: if you run ANOVA on 2 groups, the resulting p-value will be identical to the 2-tailed significance from a t-test on the same data. The same principle applies to the z-test versus the chi-square test.

Reporting 1-tailed significance is sometimes defended by claiming that the researcher is expecting an effect in a given direction. However, I cannot verify that. Second, expectations don't rule out possibilities. If somebody is absolutely sure that some effect will have some direction, then why use a statistical test in the first place? Well, it basically says that some effect is very probably not zero in some population. So is that what we really want to know? Of course not.

We really want to know how large some mean difference, correlation or other effect is. However, that's not what statistical significance tells us. For example, a correlation of 0. We conclude that the mean of variable write is different from N — This is the number of valid i. Error Mean — This is the estimated standard deviation of the sample mean. If we drew repeated samples of size , we would expect the standard deviation of the sample means to be close to the standard error.

The standard deviation of the distribution of sample mean is estimated as the standard deviation of the sample divided by the square root of sample size: 9. It is the ratio of the difference between the sample mean and the given number to the standard error of the mean: Since the standard error of the mean measures the variability of the sample mean, the smaller the standard error of the mean, the more likely that our sample mean is close to the true population mean.

This is illustrated by the following three figures. In all three cases, the difference between the population means is the same. But with large variability of sample means, second graph, two populations overlap a great deal.

Therefore, the difference may well come by chance. On the other hand, with small variability, the difference is more clear as in the third graph. The smaller the standard error of the mean, the larger the magnitude of the t-value and therefore, the smaller the p-value. We loose one degree of freedom because we have estimated the mean from the sample.

We have used some of the information from the data to estimate the mean, therefore it is not available to use for the test and the degrees of freedom accounts for this. Sig 2-tailed — This is the two-tailed p-value evaluating the null against an alternative that the mean is not equal to It is equal to the probability of observing a greater absolute value of t under the null hypothesis.

If the p-value is less than the pre-specified alpha level usually. For example, the p-value is smaller than 0. So we conclude that the mean for write is different from Mean Difference — This is the difference between the sample mean and the test value. A confidence interval for the mean specifies a range of values within which the unknown population parameter, in this case the mean, may lie. It is given by. In the example below, the same students took both the writing and the reading test.

Hence, you would expect there to be a relationship between the scores provided by each student. The paired t-test accounts for this. For each student, we are essentially looking at the differences in the values of the two variables and testing if the mean of these differences is equal to zero. In this example, the t-statistic is 0. The corresponding two-tailed p-value is 0. We conclude that the mean difference of write and read is not different from 0.

This value is estimated as the standard deviation of one sample divided by the square root of sample size: 9. This provides a measure of the variability of the sample mean. Correlation — This is the correlation coefficient of the pair of variables indicated.