- A university wants to answer: "Is there an association between hours of sleep and exam scores among first-year students?" The population is all first-year students at the university. The proposed plan is to post an online survey link on social media where students report last night's sleep hours and upload their exam score screenshot. Which aspect is most important to address before collecting data?
Answer: Ensure the sampling method reduces voluntary response bias so the sample better represents first-year students
This question focuses on voluntary response bias in sampling. Posting a survey link on social media creates severe bias - only students who see the post, are motivated to respond, and willing to share exam scores will participate. This self-selected sample likely overrepresents certain types of students (perhaps more engaged or proud of their scores) and cannot represent all first-year students. The voluntary nature of uploading exam screenshots compounds this bias. To properly study the association in the population, the study needs a sampling method that reduces voluntary response bias (B), such as random sampling with higher response rates. Increasing response time (A) would just get more biased responses. Without addressing this sampling bias, findings cannot generalize to all first-year students.
- A teacher asks: "Do students who sit in the front half of the classroom earn higher test scores than students who sit in the back half?" The population is all students in her classes. The plan is to record where each student chooses to sit throughout the unit and then compare test scores for students who usually sit in the front versus the back. Which aspect is most important to address before collecting data?
Answer: Randomly assign seating locations (front vs back) to students to reduce confounding from motivation or prior achievement
AP Statistics' introduction to planning a study emphasizes randomization in experiments to control for self-selection and confounding. Students choosing seats introduce bias, as front-sitters might be more motivated, confounding seating's effect on scores. Randomly assigning seats, as in choice A, eliminates this for causal insights. Choice B, combining schools for more students, distracts by addressing size over design flaws. Mini-lesson: Randomization prevents systematic differences between groups, isolating the variable of interest. Without it, lurking variables like motivation skew results. Strong designs yield trustworthy evidence on factors like classroom seating.
- A principal wants to answer: "Does allowing students to listen to instrumental music during independent work improve quiz scores?" The population is all students in the school. The plan is for one teacher who likes music to use music during work time in her classes, while another teacher who prefers silence keeps her usual routine; then they will compare quiz scores between the two teachers' classes. Which aspect is most important to address before collecting data?
Answer: Randomly assign students (or class periods) to music versus no music to avoid confounding with teacher differences
This question in AP Statistics' introduction to planning a study examines experimental design flaws, particularly confounding in non-randomized setups. The plan lets teachers choose music based on preference, confounding the music effect with teacher styles or class compositions. Randomly assigning music conditions, as in choice A, is vital to isolate the music's impact on quiz scores. Choice B, more quizzes for larger samples, tempts as a distractor since it increases data but ignores confounding. Mini-lesson: Randomization in experiments balances groups, reducing bias from extraneous variables like teacher differences. Without it, associations may be spurious. Always design to control for known confounders for reliable causal inferences.
- A school district wants to know whether a new 9th-grade study-skills workshop improves first-semester GPA. The research question is: "Do students who attend the workshop earn higher GPAs than those who do not?" The population is all 9th graders in the district this year. The current plan is to email an invitation to all 9th graders; students who choose to attend will be compared to students who do not attend using their semester GPAs. Which aspect is most important to address before collecting data?
Answer: Address self-selection by using random assignment to workshop vs. control (or a comparable design to reduce confounding)
This question tests understanding of confounding in observational studies. The current plan compares students who choose to attend the workshop with those who don't, but self-selection creates a major confounding issue - students who voluntarily attend are likely already more motivated or organized, which could explain any GPA differences. While increasing sample size (A) or handling missing data (C) are considerations, they don't address this fundamental design flaw. Random assignment to workshop vs. control groups (D) would eliminate the self-selection bias and allow for causal conclusions about the workshop's effectiveness. Without addressing this confounding, any observed differences in GPA cannot be attributed to the workshop itself.
- A sports scientist asks: "Does a new stretching routine reduce the number of muscle strains during the season?" The population is all athletes on a school's varsity teams. The plan is to let each coach decide whether their team will use the new routine, then compare the number of strains per athlete between teams that used the routine and teams that did not. Which aspect is most important to address before collecting data?
Answer: Randomly assign teams (or athletes) to stretching routine versus usual routine to reduce confounding by sport and coaching
In AP Statistics, introduction to planning a study focuses on experimental randomization to mitigate confounding in treatment comparisons. Letting coaches decide on the stretching routine confounds results with sport types or coaching styles, as teams choosing it might differ inherently. Random assignment to routines, per choice B, is key to establishing causality in strain reduction. Choice A, adding junior varsity for size, distracts because larger samples help precision but not bias correction. Mini-lesson: Experiments require random allocation to treatments to even out confounders across groups. Observational approaches risk misattributing effects. Prioritize design elements that support valid conclusions about interventions.
- A teacher wants to estimate the proportion of students in the entire school who prefer digital textbooks over printed textbooks. The plan is to survey the teacher's own classes because those students are easy to access. The research question is: "What proportion of all students in the school prefer digital textbooks?" Which aspect is most important to address before collecting data?
Answer: Select a sample from across the whole school (for example, a random sample by grade) to improve representativeness
This question highlights sampling bias when generalizing beyond the sampled group. The teacher's plan to survey only their own classes creates a biased sample that may not represent the entire school - students in one teacher's classes might share characteristics (grade level, subject interest, socioeconomic factors) that affect textbook preferences differently than the broader student body. Option B correctly identifies that selecting a sample from across the whole school, such as a random sample by grade, would improve representativeness and allow valid inference about all students. Surveying more of the teacher's own students (A) doesn't fix the bias. The other options about measurement scales (C), subgroup analysis (D), or survey formatting (E) are less important than ensuring the sample represents the target population. In planning studies, the sampling frame must match the population about which conclusions will be drawn.
- A state transportation agency asks: "Do digital roadside signs that display a driver's current speed reduce average driving speed?" The population is all drivers on a particular highway segment. The proposed plan is to measure speeds for one week, install the digital signs, then measure speeds for the next week and compare the two weeks' average speeds. Which aspect is most important to address before collecting data?
Answer: Address potential confounding from time effects (weather, enforcement, traffic) by using a concurrent control segment or randomization
This question tests understanding of confounding in before-after studies. The plan measures speeds before and after installing signs, but many factors could change between weeks: weather conditions, traffic patterns, police enforcement, holidays, or random variation. These time-related confounders could explain any speed differences, not the signs themselves. Using a concurrent control segment without signs (B) or randomizing sign installation across multiple segments would control for these temporal effects. Simply increasing measurements (A) or collecting more weeks (E) doesn't address the confounding. Without controlling for time effects, the study cannot determine if speed changes are due to the signs or other factors that varied between the two weeks.
- A principal wants to estimate the proportion of parents who are satisfied with school communication. The plan is to send a survey link by email and analyze only the first 200 responses received. The research question is: "What proportion of all parents at this school are satisfied with school communication?" Which aspect is most important to address before collecting data?
Answer: Ensure the responses are not biased toward parents who respond quickly by using a sampling plan that represents all parents
This question highlights response bias in survey sampling. The principal's plan to analyze only the first 200 responses creates bias because parents who respond quickly may systematically differ from the general parent population - they might be more engaged, have stronger opinions, or have more time available. Option B correctly identifies that ensuring responses aren't biased toward quick responders by using a proper sampling plan is most important. Simply taking the first 200 responses is a form of convenience sampling that may not represent all parents. While the sample size of 200 (A) might be adequate, how those 200 are selected matters more. The other options about question format (C), presentation style (D), or handling incomplete surveys (E) are secondary to ensuring representative sampling. In survey research, the method of selecting respondents is crucial for valid inference about the population.
- A university wants to answer: "What is the average number of hours per week that full-time students spend studying?" The population is all full-time students at the university. The plan is to survey students who are eating in the main dining hall between 6–7 p.m. on a weekday and ask them to estimate their weekly study hours. Which aspect is most important to address before collecting data?
Answer: Use a random sample of full‑time students (for example, from the registrar's list) to reduce undercoverage
AP Statistics introduces planning a study by stressing representative sampling to minimize bias in estimating population parameters. Surveying only dining hall students at a specific time causes undercoverage of those not eating there, like commuters or evening studiers, biasing average study hours. Using a random sample from the full-time student list, as in choice A, addresses this for better representation. Choice B, surveying longer for more responses, is a distractor; it enlarges the sample but perpetuates the bias. Mini-lesson: A good sampling frame should include all population members; simple random sampling ensures every individual has an equal chance. Avoid convenience samples that exclude subgroups. This foundation allows accurate population inferences.
- A city transportation office asks: "Is average commute time different for residents who primarily use public transit versus those who primarily drive?" The population is all city residents who commute to work. The plan is to post an online survey link on the city's public transit social media pages and compare reported commute times of transit users and drivers who respond. Which aspect is most important to address before collecting data?
Answer: Use a sampling method that reaches a representative sample of all commuters, not just followers of transit pages
AP Statistics' introduction to planning a study covers sampling techniques to avoid bias in comparative studies. Posting the survey on transit social media pages introduces selection bias, as it overrepresents transit users and may not reach drivers representatively, skewing commute time comparisons. Addressing this by using a method for a representative sample of all commuters, per choice A, is essential for unbiased estimates. Choice E, extending the survey period for a larger sample, distracts because size alone doesn't correct for a non-representative frame. Mini-lesson: Ensure the sampling frame covers the entire population to prevent undercoverage; stratified or cluster sampling can help with subgroups. Biased samples lead to invalid generalizations. Planning for inclusivity strengthens the study's credibility.
- A public health department wants to estimate the mean number of sugary drinks consumed per week by adults in a city. The plan is to post an online survey link on the department's social media pages and analyze the responses received over one week. The research question is: "What is the mean weekly number of sugary drinks consumed by all adults in the city?" Which aspect is most important to address before collecting data?
Answer: Ensure the sampling method reaches a representative cross-section of adults rather than only social media followers
This question addresses selection bias in online survey sampling. The health department's plan to post a survey link on social media will likely attract responses from people who follow the department's pages - a group that may be more health-conscious or demographically different from the general adult population in the city. Option A correctly identifies that ensuring the sampling method reaches a representative cross-section of adults is the most important issue to address. Simply increasing sample size (B) won't fix this bias, as more biased data doesn't become unbiased. The other options about analysis decisions (C), measurement precision (D), or outlier handling (E) are secondary concerns that won't matter if the sample doesn't represent the target population. In planning studies, the principle of representative sampling must be established first, as biased samples lead to biased estimates regardless of sample size or analysis sophistication.
- A consumer group wants to answer: "What proportion of residents in our state support a proposed ban on single-use plastic bags?" The population is all adult residents in the state. The plan is to call phone numbers from a list of people who previously signed online environmental petitions and ask whether they support the ban. Which aspect is most important to address before collecting data?
Answer: Use a sampling frame that covers the full adult population (not just petition signers) to reduce selection bias
This AP Statistics question on introduction to planning a study addresses sampling bias in opinion surveys for population proportions. Calling only prior petition signers creates selection bias toward environmentally inclined individuals, overestimating support for the plastic bag ban. A sampling frame covering all adults, as in choice A, reduces this bias for representativeness. Choice B, more calls for larger samples, is a common distractor but doesn't fix the biased frame. Mini-lesson: Use probability sampling to mirror the population and avoid voluntary or convenience bias. A flawed frame leads to unreliable estimates. Planning inclusively ensures the sample reflects diverse views accurately.
- A city transportation office wants to compare average commute times between people who drive and people who use public transit. The plan is to interview commuters at a downtown parking garage entrance for three mornings and record commute time and mode of transportation. The research question is: "How do mean commute times compare for all city commuters who drive versus use public transit?" Which aspect is most important to address before collecting data?
Answer: Use a sampling approach that includes both drivers and transit users rather than sampling at a parking garage
This question highlights sampling bias when comparing two groups. The transportation office's plan to interview only at a parking garage entrance will systematically exclude public transit users, making it impossible to fairly compare commute times between drivers and transit users. Option B correctly identifies that the sampling approach must include both groups of interest - drivers AND transit users - to make valid comparisons. Interviewing only at a parking garage creates a sample that consists primarily or entirely of drivers. While increasing sample size (A), standardizing measurement (C), choosing analysis methods (D), or ensuring consistency (E) are all good practices, they cannot overcome the fundamental flaw of missing one entire group from the comparison. When planning comparative studies, researchers must ensure their sampling frame includes adequate representation from all groups being compared.
- An environmental group asks: "Do households that receive a water-conservation brochure reduce monthly water use?" The population is all households in a town. The proposed plan is to mail brochures to households that have emailed the group in the past, then compare their next month's water use to the townwide average for that month. Which aspect is most important to address before collecting data?
Answer: Use a randomized comparison group from the town (and avoid selecting only prior supporters) to reduce bias and confounding
This question tests recognition of selection bias and confounding. The plan sends brochures only to households that previously emailed the environmental group - these are likely already environmentally conscious households who may have been reducing water use anyway. Comparing them to the town average creates both selection bias and confounding. These motivated households don't represent typical town residents, and their water use patterns may differ for reasons unrelated to the brochure. Using a randomized comparison group from the general town population (C) would eliminate this bias and allow valid conclusions about the brochure's effect. Without proper randomization and avoiding selection of prior supporters, any observed differences cannot be attributed to the brochure itself.
- A fitness app company asks: "Does enabling a daily step-goal notification increase average daily steps?" The population is all current app users. The proposed plan is to compare average steps for users who turn on notifications in the settings to users who do not, using one month of app data. Which aspect is most important to address before collecting data?
Answer: Randomly assign notification status (or use a design that addresses confounding) because users who opt in may already be more motivated
This question focuses on self-selection bias and confounding. Users who choose to enable step-goal notifications are likely already more motivated about fitness than those who don't enable them. This self-selection creates confounding - any observed differences in steps could be due to pre-existing motivation levels, not the notifications themselves. Random assignment of notification status (B) would eliminate this confounding by ensuring groups are comparable except for notification status. Simply increasing sample size (A) or data collection time (E) won't address this fundamental design issue. Without randomization or other methods to address confounding, the study cannot determine if notifications actually increase steps or if motivated users both enable notifications and walk more.
- A school nurse wants to estimate the proportion of students who get at least 8 hours of sleep on school nights. The nurse plans to survey only students who come to the nurse's office during one week. The research question is: "What proportion of all students at this school get at least 8 hours of sleep on school nights?" Which aspect is most important to address before collecting data?
Answer: Select students using a method that is likely to represent the whole student body, not just nurse visitors
This question tests recognition of selection bias in health-related surveys. The nurse's plan to survey only students visiting the nurse's office creates a biased sample, as these students may be experiencing health issues that could be related to sleep deprivation, making them unrepresentative of the general student body. Option B correctly identifies that selecting students using a method likely to represent the whole student body is most critical. Students who visit the nurse may systematically differ in their sleep patterns from those who don't. While increasing the sample size of nurse visitors (A) might improve precision, it won't fix the bias. The other options about measurement details (C), subgroup analysis (D), or survey formatting (E) are less important than ensuring the sample represents all students. In planning studies about population characteristics, avoiding selection bias through proper sampling is paramount.
- A public health department asks: "What proportion of adults in our county got a flu shot this season?" The population is all adults living in the county. The proposed plan is to stand outside a large gym on Saturday morning and survey as many adults as possible about whether they received a flu shot. Which aspect is most important to address before collecting data?
Answer: Switch to a probability sampling method (such as an SRS of county adults) to improve representativeness
In AP Statistics, introduction to planning a study emphasizes selecting appropriate sampling methods to ensure representativeness and reduce bias. The plan uses a convenience sample at a gym, which likely undercovers non-gym-goers or those unavailable on Saturday mornings, leading to biased estimates of the county's flu shot proportion. Switching to a probability sampling method like a simple random sample (SRS), as in choice A, is crucial to give every adult an equal chance of selection and improve generalizability. Choice B, increasing sample size via more surveyors, might seem appealing for precision, but it doesn't address the underlying bias in who is sampled. A mini-lesson on planning studies: Use random sampling to avoid selection bias and ensure the sample mirrors the population, enabling reliable inferences. Non-probability samples, like convenience ones, often lead to undercoverage or voluntary response bias. Prioritizing representativeness is key before data collection.
- A local news station asks: "What proportion of city residents support building a new sports arena?" The population is all adult residents of the city. The proposed plan is to stand outside the arena at a weekend event and interview as many adults as possible about whether they support the new arena. Which aspect is most important to address before collecting data?
Answer: Use a random sample of adult residents (e.g., random digit dialing or address-based sampling) rather than a convenience sample at the arena
This question tests understanding of sampling bias in surveys. Interviewing people outside the arena at an event creates severe bias - attendees at arena events are likely sports fans who support arena construction more than typical residents. This convenience sample cannot represent all adult city residents' opinions. To estimate the true proportion of city residents who support the arena, the study needs a random sample of residents (A) through methods like random digit dialing or address-based sampling. Interviewing more people at the arena (B) just increases the biased sample size. Without proper random sampling from the population, the results will overestimate support by capturing mainly arena event attendees rather than representative city residents.
- An environmental club wants to estimate the average number of plastic bottles used per day by students at their school. The plan is to count bottles in the recycling bins located near the gym for five school days and divide by the number of days. The research question is: "What is the mean number of plastic bottles used per day by all students at this school?" Which aspect is most important to address before collecting data?
Answer: Ensure the measurement method captures bottles used by the whole school, not mainly gym users or specific locations
This question tests understanding of measurement bias in estimation. The environmental club's plan to count bottles only in recycling bins near the gym will likely produce a biased estimate because gym users may have different bottle usage patterns than the general student population, and bottles disposed elsewhere won't be counted. Option B correctly identifies that the measurement method must capture bottles used by the whole school, not just those in specific locations or by specific groups. Counting only gym-area recycling misses bottles thrown in trash, recycled elsewhere, or taken home. While increasing observation days (A) might reduce variability, it won't fix the systematic undercounting or bias. The other options about reporting statistics (C), checking distributions (D), or collecting extra information (E) are less critical than ensuring the measurement captures the full scope of bottle usage.
- A company wants to answer: "Do employees who work from home at least 3 days per week report higher job satisfaction?" The population is all employees at the company. The proposed plan is to survey employees about job satisfaction and categorize them by whether they currently work from home at least 3 days per week, then compare average satisfaction scores. Which aspect is most important to address before collecting data?
Answer: Use random assignment to work-from-home vs. in-office (or otherwise account for confounding) before making a cause-and-effect claim
This question tests recognition of confounding in observational studies. The plan compares employees who already work from home with those who don't, but this is observational - employees weren't randomly assigned to work arrangements. Many confounding factors could explain satisfaction differences: job type, seniority, personal circumstances, or company policies about who can work remotely. Without random assignment (B) or other methods to control confounding, any observed differences in satisfaction cannot be attributed to the work-from-home arrangement itself. Increasing sample size (C) or changing statistical tests (D) won't address this fundamental design limitation. The study needs experimental control to make causal claims about work-from-home effects on satisfaction.
