• We’ve found that many teenagers tend to be curious and open to having discussions about mental health and social media. However, that openness quickly fades if they feel that they’re being “given a lecturing” about social media. Starting the lesson with the examples (in the lesson slides and video) of past concerns about radio, television, and video games provides a helpful “we know some concerns have been overblown in the past” framing to the lesson. Then, presenting the more recent statistics about mental health declines feels less like a lecture, and more like the presentation of a compelling question: Are these trends just associated with the spread of social media, or are they causally connected?
• Many students are frequent social media users. Bringing that expertise into the room can feel empowering, and we find that students are often excited to dive into this lesson and share their own experiences. At the same time, other students may not feel as comfortable discussing their own experience with social media, especially as it relates to mental health. We recommend keeping this lesson’s discussions at the statistical level, while also allowing students to share their own experiences according to their own level of comfort.
• It’s helpful to focus on the fact that the sampling distribution in this lesson is a distribution of differences in sample means. This can help students distinguish the values in the sampling distribution from individual depression scale scores or individual sample means.

First, download this lesson's Handout Key and read through its Discussion Question section. Then, check out our model discussion norms and the additional background notes below.

• If students are stuck, here’s a helpful framing question to get them started: “Is a 5.8 unit difference surprising? Or could it happen by chance? How can you tell?” This will help students identify that they have to use the sampling distribution to judge whether a difference is unusual or not.
• If students need support understanding the concept of the sampling distribution, ask them to imagine repeatedly shuffling participants between two groups. Imagine no treatment is being applied to either group. For each shuffle, they find the difference in the mean depression scale scores between the two groups. These differences compose the sampling distribution, and they provide a reference for judging what we could expect from random assignment alone.

• The opening of the lesson mentions increased rates of major depressive episodes, anxiety disorders, and emergency department visits for self-harm among young people. It’s possible that the first two indicators could be attributed to more frequent detection and reporting of mental health disorders, rather than true increases in the underlying prevalence of these disorders. However, the same can’t be said about the third metric, since standards for reporting among emergency departments have not changed over the time periods mentioned in the lesson.
• Students may wonder: In the Iowa State social media experiment, why did researchers only ask participants to limit their social media use, rather than fully controlling it? In the full paper, the researchers state, “One consistent finding has been that complete abstinence from social media may not be sustainable for the average user. A less strict approach is to limit social media use by monitoring. Monitoring limited usage, as opposed to abstinence, may be more sustainable and practical.” So, researchers wanted to know if a more sustainable approach that required less coercion – merely telling folks to reduce their social media time – would still produce an effect. And it did.
• Students interested in exploring this topic further can check out the increasing body of experimental research on social media use and mental health, including the following articles: Brailovskaia, Swarlik, Grethe, Schillack, & Margraf (2023), Davis & Goldfield (2025), Graham, Mason, Riordan, Winter, & Scarf (2021), Hunt, All, Burns, & Li (2021), Kleemans, Daalmans, Carbaat, & Anschütz (2018), Lambert, Barnstable, Minter, Cooper, & McEwan (2022), Thai, Davis, Mahboob, Perry, Adams, & Gold (2023), and Yuen, Koterba, Stasio, et al. (2019).

• Emphasize the distinction between the difference in population means, μ₁ − μ₂, and the difference in sample means, x̄₁ − x̄₂. The population difference has a fixed value, although we typically do not know what that value is. The sample difference is a statistic that can vary across different samples or different random assignments.
• Check out the optional video here for more information about how the formula for the standard deviation of the sampling distribution is derived. For the AP Exam, students need to be able to recognize the formula and use it. They don’t need to know its derivation. However, for students who are curious about where the formula comes from, this resource is helpful and provides a window into more advanced statistics coursework.
• Technically, because this lesson describes an experiment with random assignment, the distribution used for inference is a randomization distribution rather than a sampling distribution. Randomization distributions describe the results of repeatedly reassigning treatments, whereas sampling distributions describe the results of repeatedly drawing random samples. For these procedures, however, the mathematical results are equivalent, so AP Statistics does not formally distinguish between the two.