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The image describes a statistical example involving a survey of human resource professionals. Here's a breakdown of the problem:

- **Survey information**: Out of 410 human resource professionals surveyed, 148 said that job candidates were disqualified due to information found on social media.

- **Objective**: The aim is to use this sample to make broader conclusions about the population of all human resource professionals.

Now let's fill in the blanks:

1. **Population**: All human resource professionals.
   
2. **Sample**: The 410 human resource professionals who were surveyed.

This data can be used to infer trends and make predictions about the entire population, utilizing statistical methods.

Would you like a more detailed explanation on sampling techniques or inference methods? 

Here are 5 related questions you could consider:
1. How is the sample size determined in such surveys?
2. What is the significance of the proportion of professionals who disqualified candidates?
3. How can we generalize findings from this sample to the entire population?
4. What methods are used to ensure that a sample is representative of the population?
5. How can this survey's findings impact hiring practices in HR?

**Tip**: When drawing conclusions from samples, it's essential to ensure that the sample is random and representative to avoid biased results.

In a survey of 410 human resource professionals, 148 said job candidates were disqualified because of information found on social media. Use this sample to draw conclusions about the population of all human resource professionals.

Explore how a survey of 410 HR professionals, where 148 reported disqualifying candidates due to social media information, helps draw conclusions about all HR professionals. Learn about statistical concepts like sampling, proportions, and population inference.

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