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In survey research, it's crucial to estimate how many people will respond to your invitation. This is called the 'expected responses.' Given a known response rate, you can predict the number of responses you will receive. For instance, in our exercise, the researcher knows the typical response rate is 40%.
To calculate the expected responses, you can use the formula: \( \text{Expected responses} = \text{Total e-mails} \times \text{Response rate} \).
Plugging in the numbers from our exercise: \( \text{Expected responses} = 1500 \times 0.40 = 600 \).
This means the researcher can expect about 600 people to respond to her e-mail survey. Understanding this helps in planning how many people you need to contact to get a sufficient number of completed surveys.

An unbiased sample is one where every member of the population has an equal chance of being included. This is important because it ensures that the results of your survey truly represent the population you're studying.
In the exercise, if the researcher selects 1500 e-mail addresses randomly, the sample is likely to be unbiased, assuming several conditions:

• The list of e-mail addresses represents the entire population accurately.
• The response rate does not differ significantly among different subgroups within the population.

If these conditions are met, the 300 respondents (even though we expect 600, only 300 are needed) are likely to form an unbiased sample. This means the survey results will be more reliable and generalizable to the whole population.

Random selection is a key technique in survey research to avoid bias. It means each member of the population has an equal chance of being selected. This method helps ensure the sample reflects the diversity of the population.
In the provided exercise, the researcher randomly selects 1500 e-mail addresses from a larger list. This random selection process is crucial for two main reasons:

• Prevents favoritism towards any particular group within the population.
• Increases the likelihood that the sample will be representative of the broader population.

For instance, if only e-mails from a specific region or demographic were selected, the survey would be biased and the results not generalizable. Thus, random selection helps improve the reliability and validity of the survey findings.