Develop the statistical assumption

What is the statistical assumption?
What would be considered the null hypothesis?
The alternative hypothesis?

Use the following information:

The test of interest in this problem is the t-test for comparing two population means, the population mean hours worked last week for males and the population mean hours worked last week for females. The first set of data to be aware of under Levene's test for equality of variances column"F" and"Sig". The F value is 1.175, and the significance level of this test, also known as the"p-value" is .279. This data is insufficient to show the variances are different. To analyze the following data we shall refer to all lines labeled "Numbers of hours worked last week" and "Equal variances assumed." This assumption, along with the assumption that the sample sizes are large enough for the sampling distributions of the sample means to be normally distributed. The projection only confirms the necessity to use the two-subject t-test.

The Sig (two-tailed) projected a value of 0.000. This means that if the population means were the same, we have seen an event that would occur less than once in 1000 tries this is very strong evidence that the two population means are different.

At a 95% confidence interval and a degree of freedom of 1488, the two end-points at this interval were observed to be 8.374 and 13.050. Due to the positive nature of the interval, there is strong evidence that the mean of population 1 is greater than the mean of population 2.

In summation, both a hypothesis test and a confidence interval test were performed for comparison of the two population means. The procedures used were t-test, independent sampling, assuming equal variances, and sample means having a normal distribution. The p-value for the hypothesis test was very small, strong evidence that the two means are different. The confidence interval did not contain zero. Evidence at the 95% level of confidence show further proof that the two means are different.