SPSS ANALYSIS

SPSS Analysis

SPSS Analysis

One sample t-test

A one-sample t test is a hypothesis test for determining whether the mean of a population is different from some known (test) value. The researcher begins by selecting a sample of observations from the population of interest and estimates the population mean by calculating the mean of the sample. The researcher then compares this sample mean with the test value of interest via the formula where is the sample mean, µ is the test value, s is the sample standard deviation, and n is the sample size. This t value can then be used to determine the likelihood that any difference between the sample mean and the test value is real versus a result of chance.

For example, a researcher studying the better-than-average effect might be interested in determining whether students, on average, think they are more athletic than the average student. Thus, the researcher has participants rate on a 1-7 scale (where 1 = below average, 4 = average, 7 = above average) their athletic ability and calculates the sample mean. Because the researcher is not interested in the sample per se but in the population of college students, he or she needs to determine whether any difference between the sample mean and “4” (the test value) is real or caused by chance. The one-sample t test will assist in making this determination.

Adult and Baby

1-Sample Statistics

N

Mean

Std. Deviation

Std. Error Mean

Preference (A and B)

33

3.3333

1.42887

.24873

Effectiveness(A and B)

31

3.0000

1.78885

.32129

1-Sample Test

Test Value=0

t

df

Sig. (2-tailed)

Mean Difference

95 % Confidence Interval of the Difference

Lower

Upper

Preference (A and B)

13.401

32

.000

3.33333

2.8267

3.8400

Effectiveness(A and B)

9.337

30

.000

3.00000

2.3438

3.6562

Interpretation of results

The result of a one-sample t test is a t value. This t value is compared with the t -distribution that would occur if the population mean were equal to the test value. This possibility (that the population mean is equal to the test value) is known as the null hypothesis. The logic that underlies the one-sample t test is to compare the observed value of t (that is, the value of t calculated from the one-sample t test) to the t -distribution that would occur if the null hypothesis were true. If the observed value of t is in keeping with this distribution, then the conclusion is that the null hypothesis might be true (i.e., the population mean is equal to the test value). If the observed value of t is not in keeping with this distribution, then the conclusion is that the null hypothesis is not true (i.e., the population mean is not equal to the test value).The one sample T test has been performed on 95% level of confidence between the selected variables to find out the significant difference between the sample mean.

The test results state that the significant test value of all the variables is 0.000 i.e. less than 5% level of significance. Therefore; it is concluded that there is a significant difference between the mean, selected variables.

In one sample T-test table, we will compare sample mean with population mean. The null hypothesis is tested in this analysis is:

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