## Description

**About:**

This discussion provides you an opportunity to look at an example of how to complete lab 2 together with other students!

**About the Research:**

First, let's get an idea of what the researcher was trying to do here: this researcher is interested in seeing if there is a relationship between **sex (male/female)** and **hours worked** per **week.** They receive the following statistical output.

**Statistical Output Begins:(If you are not sure what these terms mean, please review our video lesson, lesson notes, and review chapter 8 in our textbook)**

*Descriptive Statistics Hours worked by Sex*

Samples | N | Mean | Variance |

Males | 1212 | 32.7 hours | 19.2 |

Females | 1333 | 26.5 hours | 21.8 |

*T-statistic Output*

T-statistic | degrees of freedom | One-Tailed (Sig.) | Two-Tailed (Sig.) |

5.443 | 2543 | p<.001 | p<.001 |

This Statistical Output comes from the General Social Survey, 2022 data and was calculated using a software called SPSS.

**Example Report Based on the Above Data:**

**Now, here is an example report! Please read it carefully and use it as an example for future use.**

**Title:** Examining Gender Differences in Hours Worked

**Introduction:** Based on the tables provided, this study aims to investigate whether there are significant differences in the number of hours worked between males and females in a given population. The independent variable is "sex/gender" (male vs female) and the dependent variable of interest is "hours worked". The Null Hypothesis (H0) suggests that there is no significant difference in the mean hours worked between the two groups, while the Research Hypothesis (H1) suggests that there is a significant difference.

**Methods:** The data was collected from a total of 2,545 individuals, with 1,212 males and 1,333 females. The mean hours worked for males (Mean 1) was 32.7 hours, and the variance for males was 19.2. For females, the mean hours worked (Mean 2) was 26.5 hours, and the variance was 21.8.

We conducted an independent samples t-test to compare the means of hours worked between the two groups. The t-statistic was calculated to be 5.443, and the degrees of freedom (df) were 2,543. The p-value associated with this test was less than 0.001, indicating a very low probability of obtaining such results by random chance.

**Results:** The results of the independent samples t-test revealed a significant difference in the number of hours worked between males and females in the population (t = 5.443, p<.001). This means that we reject the null hypothesis (H0) in favor of the Research Hypothesis. There is strong evidence there is a significant relationship between sex and hours worked. It appears males work more hours than females in the larger populations.

**Discussion:** The differences discovered in this study highlight gender differences in society. Perhaps more males are considered breadwinners in their families and are expected to work more hours. While more females are expected to care for children by their family and not expected to work as many hours. There are many other possible variables that we did not examine that could play a role as well such as age (people that are retired may not work), ability, education, family situation, and many other social and economic factors.

In conclusion, based on the statistical analysis and the provided data, we can confidently reject the null hypothesis and conclude that there is a significant difference in the number of hours worked between males and females in this population.

**Prep-Lab Discussion Directions (What to Do Now):**

To help each other understand the statistical output and the report, in your main post simply answer at least **two** of the following questions!

Answer **Two** in your discussion:

1. What were the two variables we examined and which was the independent and dependent variable?

2. Why did the researcher reject the null hypothesis? What symbol do we look at to determine this?

3. What happens when you get a p-value that is less than .05? Do you accept or reject the null hypothesis?

4. What does it mean to reject the null hypothesis? How do we interpret this?

5. What additional variables other than sex (male/female) could play a role in the dependent variable?

6. What were the sample size of the male and female groups? What was the mean of the male and female groups?

7. When would we accept the null hypothesis? How do we interpret our results if we accept the null hypothesis?

## Explanation & Answer

Attached.

1

Statistic Question

Name

Institution

Course

Professor

Date

2

Statistic Question

Why did the researcher reject the null hypothesis? What symbol do we look at to

determine this?

The researcher dismissed the null hypothesis because the p-value from the independent

samples t-test was under 0.001. The p-value is a key statistical indicator that reveals the

probability of observing the particular data or even more extreme results from the point of view

of the null hypothesis. In this setting, a p-value ...