Question

Here is an example of T-test down blow (it doesn't have to be exactly) I need to help with my data which it is about phone service survey. Please help and I really appreciate your time.

T-test looked at how far people travel to visit a healthcare clinic compared to how easy it was to understand the information that their physician was explaining to them (Table 2). The sample group was divided into two categories: people who travel less (?5 miles) and people who travel more (?6 miles). Table 2 shows that, on average, people who travel less understood more information that their physician was explaining to them than the people that traveled more (people who travel less = 4.63, people who travel more = 4.10, p = .026). The conclusion that could be drawn from this finding is that physicians who work in clinics close to dense populations are better at explaining information to their patients. This could be due to these physicians seeing more patients with similar conditions, making it easier for them to explain information to their patients with similar conditions.

Table 2. Distanced Normally Traveled vs. How Easy Information was Explained by Physician

Group Statistics

	NormTravel.re	N		Mean	Std. Deviation	Std. Error Mean
Information	People who travel less People who travel more		19	4.6316	.49559	.11370 .19401
			21	4.0952	.88909

Independent Samples Test

	Levene's Test for Equality of Variances				t-test for Equality of Means
	F	Sig.	t	df	Sig. (2tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
								Lower	Upper
Information Equal variances assumed Equal variances not assumed	3.098	.086	2.322	38 31.914	.026 .023	.53634	.23102	.06866 .07824	1.00402
			2.385			.53634	.22488		.99444

Table 2. Distanced Normally Traveled vs. How Easy Information was Explained by Physician

Group Statistics

	NormTravel.re	N		Mean	Std. Deviation	Std. Error Mean
Information	People who travel less People who travel more		19	4.6316	.49559	.11370 .19401
			21	4.0952	.88909

Independent Samples Test

	Levene's Test for Equality of Variances				t-test for Equality of Means
	F	Sig.	t	df	Sig. (2tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
								Lower	Upper
Information Equal variances assumed Equal variances not assumed	3.098	.086	2.322	38 31.914	.026 .023	.53634	.23102	.06866 .07824	1.00402
			2.385			.53634	.22488		.99444

Here is my data down blow and I need help with interpret or explain just like the exmpale. Thank you so much!

Group Statistics
	Gender	N	Mean	Std. Deviation	Std. Error Mean
Overall Satisfaction	Male	17	4.35	.786	.191
	Female	18	4.11	.832	.196

Levene's Test for Equality of Variances					t-test for Equality of Means
		F	Sig.	T		df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95 % confidence interval of the difference
										Lower	Upper
Overall Satisfaction	Equal variances assumed	.009	.927	.883		33	.384	.242	.274	-.316	.799
	Equal variances assumed			.884		33.000	.383	.242	.274	-.315	.798

Answer 1

From given output, it is observed that the average overall satisfaction score regarding phone service for male respondents is given as 4.35 with the standard deviation of 0.786. The average overall satisfaction score regarding phone service for female respondents is given as 4.11 with the standard deviation of 0.832.

Here, we have to check or test the hypothesis or claim whether there is any significant difference exists between the average satisfaction score for male respondents and female respondents. For checking this hypothesis we have to use two sample t test or independent samples t test. Here, we are not given the values for the population standard deviations and that’s why we are using t-test. The null and alternative hypotheses for this test are given as below:

Null hypothesis: H₀: There is no any significant difference in the average overall satisfaction score between the male and female respondents.

Alternative hypothesis: H_a: There is a significant difference in the average overall satisfaction score between the male and female respondents.

For checking the significance for equal variances, P-value for F test is given as 0.927 which is greater than alpha value 0.05, so we conclude that population variances are equal. Corresponding t test gives us the P-value of 0.384 which is greater than alpha value 0.05, so we do not reject the null hypothesis that there is no any significant difference in the average overall satisfaction score between the male and female respondents.

There is no sufficient evidence to conclude that there is a significant difference in the average overall satisfaction score between the male and female respondents.