Example: A school district polled 400 people to determine if it was a good idea to not have school on Friday. 30% of people responded that it was not a good idea to have school on Friday. Predict the approximate percentage of people who think it would be a good idea to have school on Friday from a population of 6,228 people.
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Given categorical data from a random sample, use proportional relationships to make predictions about a population.
Standard #: MA.7.DP.1.3
Standard Information
Standard Examples
Example: O’Neill’s Pillow Store made 600 pillows yesterday and found that 6 were defective. If they plan to make 4,300 pillows this week, predict approximately how many pillows will be defective.
General Information
Subject Area: Mathematics (B.E.S.T.)
Grade: 7
Strand: Data Analysis and Probability
Date Adopted or Revised: 08/20
Status: State Board Approved
Standard Instructional Guide
Connecting Benchmarks/Horizontal Alignment
Terms from the K-12 Glossary
- Categorical Data
- Population (in Data Analysis)
- Proportional Relationships
- Random Sampling
Vertical Alignment
Previous Benchmarks
Next Benchmarks
Purpose and Instructional Strategies
In grade 6, students described data using measures of center and variation. In grade 7, students use samples to compare measures of center and variation in data sets, as well as use samples to make a generalization about the population from which the sample was taken. In grade 8, students will use bivariate data to study proportional and linear relationships and make predictions with lines of fit. In high school, students will estimate a population total, mean or percentage using data from a sample survey and develop a margin of error through the use of simulation.- Instruction includes helping students understand that since there is always variability in collecting samples. These generalizations, or predictions, can only be estimates of what we expect to see from the greater population.
- Use real-world scenarios to explain that random sampling is needed when we need to find information about a population that is too large or too difficult to measure completely (MTR.7.1).
- As in the second example for this benchmark, the school district polled 400 people because it would be difficult, and perhaps costly, to poll all 6,228 people efficiently. Therefore, we can analyze a sample that is representative of the population to get an idea of what may be happening with the larger group.
- Students have done more precise work with proportional relationships in MA.7.AR.4, but in this benchmark, students will use the same proportional reasoning to make predictions or find estimates of what may be happening in a population that is too large or cumbersome for us to measure completely.
- Instruction includes having students make predictions about what the population measures will be, based on the sample (MTR.6.1).
- Instruction uses manipulatives or simulations to have students collect their own set of data (MTR.2.1).
- As in the first example for this benchmark about defective pillows, students can pull a random sample from a bag of chips that have been strategically marked D for defective or have no marking for no defects. They can use their proportion of defective chips to predict how many might be in the entire bag. Comparisons can be made across the different groups in the room to see how close the estimates were to the actual values (MTR.4.1).
Common Misconceptions or Errors
- Students may mistake part to total as part to part, which would give an incorrect ratio when setting up their proportion. To address this misconception, use percentages or counts out of 100 to help illustrate this more clearly.
- Students may not understand what random sampling is or why it is important. To address this misconception, allow students to collect random samples and make comparisons across groups to show they are not exact, but representative, of the larger population.
Strategies to Support Tiered Instruction
- Teacher provides instruction focused on color-coding and labeling the different categories based on the sample and the population when setting up a proportional relationship to ensure corresponding parts are placed in the corresponding positions within the proportion.
- For example, based on a random sample of 150 people, 23 people stated that they preferred to go grocery shopping on Saturday morning. If ones wants to make a prediction on how many people, out of a town of 4500 people, who prefer to go shopping on Saturday morning, the proportion below can be used.Students can also make the connection to multiplicative relationships between shoppers and Saturday shoppers as shown below.
- For example, based on a random sample of 150 people, 23 people stated that they preferred to go grocery shopping on Saturday morning. If ones wants to make a prediction on how many people, out of a town of 4500 people, who prefer to go shopping on Saturday morning, the proportion below can be used.
- Teacher uses percentages to help illustrate the difference between part to part and part to total more clearly. Use percentages or counts out of 100 to help illustrate this more clearly.
- Instruction includes providing students with an example of random sampling and biased sampling in a context that is relevant to students.
- For example, Branden at Sunshine Middle School wants to predict if pizza should be served at the Fall Festival and Johnny suggests sampling 20 students in his 7th grade class. Amanda suggests it would be better to sample 20 random students from all grade levels at Sunshine Middle School. Since Branden wants to ensure that the sampling is not biased, he chooses Amanda’s plan since the prediction is for the whole school and not just for one class at the school.
- Instruction includes the use of problems with percentages or counts of 100.
- For example, Mr. Smith surveyed students at his school last year to determine which candy bar they preferred. His results are shown in the pie chart below. Students can discuss how they can use this information to make a prediction about how many students prefer a certain candy bar this school year.
- For example, Mr. Smith surveyed students at his school last year to determine which candy bar they preferred. His results are shown in the pie chart below. Students can discuss how they can use this information to make a prediction about how many students prefer a certain candy bar this school year.
- Teacher allows students to collect random samples and make comparisons across groups to show they are not exact, but representative of the larger population
Instructional Tasks
Instructional Task 1 (MTR.4.1, MTR.5.1)A random sample of the 1,200 students at Moorsville Middle School was asked which type of movie they prefer. The results are compiled in the table below:

- Part A. Use the data to estimate the total number of students at Moorsville Middle school who prefer horror movies.
- Part B. Use the data to estimate the total number of students at Moorsville Middle school who prefer either mystery or science fiction movies.
- Part C. Suppose another random sample of students was drawn. Would you expect the results to be the same? Explain why or why not.
Instructional Task 2 (MTR.4.1, MTR.7.1)
A constitutional amendment is on the ballot in Florida, and it needs at least 60% of the vote to pass. The editor of a local newspaper wants to publish a prediction of whether or not the amendment will pass. She hires ten pollsters to each ask 100 randomly selected voters if they will vote yes.

Instructional Items
Instructional Item 1A research group is trying to determine how many alligators are in a particular area. They tagged 30 alligators and released them. Later, they counted 12 alligators who were tagged out of the 150 they saw. What can the research group estimate is the total population of alligators in that area?
Instructional Item 2
The local grocery store is going to donate milk and cookies to an upcoming middle school event. They surveyed 150 students in the school to determine which type of milk they prefer and recorded the results below.

*The strategies, tasks and items included in the B1G-M are examples and should not be considered comprehensive.
Related Courses
- M/J Accelerated Mathematics Grade 6 (Specifically in versions: 2014 - 2015, 2015 - 2020, 2020 - 2022, 2022 - 2024, 2024 and beyond (current)) # 1205020
- M/J Grade 7 Mathematics (Specifically in versions: 2014 - 2015, 2015 - 2022, 2022 - 2024, 2024 and beyond (current)) # 1205040
- M/J Foundational Skills in Mathematics 6-8 (Specifically in versions: 2014 - 2015, 2015 - 2022, 2022 - 2024, 2024 and beyond (current)) # 1204000
- Access M/J Grade 7 Mathematics (Specifically in versions: 2014 - 2015, 2015 - 2018, 2018 - 2019, 2019 - 2022, 2022 and beyond (current)) # 7812020
Related Access Points
- MA.7.DP.1.AP.3 # Given data from a random sample of the population, select from a list an appropriate prediction about the population based on the data.
Related Resources
Formative Assessments
- Prediction Predicament # Students are asked to use sample data to make and assess a prediction.
- School Days # Students are asked to use data from a random sample to estimate a population parameter and explain what might be done to increase confidence in the estimate.
- Movie Genre # Students are asked to use data from a random sample to draw an inference about a population.
Lesson Plans
- Election Predictions # Students will examine poll results from three cities to predict a voting outcome on a local level. They will make inferences about a population based on the poll results and develop a written statement to present their findings to the board of county election commissions. Students will then use the peojected election results to determine the impact of citizens in the community.
- Is My Backpack Too Massive? # This lesson combines many objectives for seventh grade students. Its goal is for students to create and carry out an investigation about student backpack mass. Students will develop a conclusion based on statistical and graphical analysis.
- Cricket Songs # Using a guided-inquiry model, students in a math or science class will use an experiment testing the effect of temperature on cricket chirping frequency to teach the concepts of representative vs random sampling, identifying directly proportional relationships, and highlight the differences between scientific theory and scientific law.
- Computer Simulated Experiments in Genetics # A computer simulation package called "Star Genetics" is used to generate progeny for one or two additional generations. The distribution of the phenotypes of the progeny provide data from which the parental genotypes can be inferred. The number of progeny can be chosen by the student in order to increase the student's confidence in the inference.
Perspectives Video: Experts
- Statistical Sampling Results in setting Legal Catch Rate # Fish Ecologist, Dean Grubbs, discusses how using statistical sampling can help determine legal catch rates for fish that may be endangered. Download the CPALMS Perspectives video student note taking guide.
- Assessment of Antarctic Ice Sheet Movement Rate by Sediment Core Sampling # <p>Eugene Domack, a geological oceanographer, describes how sediment cores are collected and used to estimate rates of ice sheet movement in Antarctica. Video funded by NSF grant #: OCE-1502753.</p>
- Mathematically Modeling Hurricanes # <p>Entrepreneur and meteorologist Mark Powell discusses the need for statistics in his mathematical modeling program to help better understand hurricanes.</p>
- Using Statistics to Estimate Lionfish Population Size # <p>It's impossible to count every animal in a park, but with statistics and some engineering, biologists can come up with a good estimate.</p>
- Tow Net Sampling to Monitor Phytoplankton Populations # <p>How do scientists collect information from the world? They sample it! Learn how scientists take samples of phytoplankton not only to monitor their populations, but also to make inferences about the rest of the ecosystem!</p>
- Managing Lionfish Populations # <p>Invasive lionfish are taking a bite out of the ecosystem of Biscayne Bay. Biologists are looking for new ways to remove them, including encouraging recreational divers to bite back!</p>
Perspectives Video: Professional/Enthusiasts
- Nestle Waters & Statistical Analysis # <p>Hydrogeologist from Nestle Waters discusses the importance of statistical tests in monitoring sustainability and in maintaining consistent water quality in bottled water.</p>
- Fishery Independent vs Dependent Sampling Methods for Fishery Management # <p>NOAA Scientist Doug Devries discusses the differences between fishery independent surveys and fishery independent surveys. Discussion includes trap sampling as well as camera sampling. Using graphs to show changes in population of red snapper.</p>
- Camera versus Trap Sampling: Improving how NOAA Samples Fish # <p>Underwater sampling with cameras has made fishery management more accurate for NOAA scientists.</p>
- Making Inferences about Wetland Population Sizes # <p>This ecologist from the Coastal Plains Institute discusses sampling techniques that are used to gather data to make statistical inferences about amphibian populations in the wetlands of the Apalachicola National Forest.</p>
- Random Sampling to Estimate Wildlife Populations # <p>Dr. Bill McShea from the Smithsonian Institution discusses sampling and inference in the study of wildlife populations.</p> <p>This video was created in collaboration with the Okaloosa County SCIENCE Partnership, including the Smithsonian Institution and Harvard University.</p>
- Sampling Bird Populations to Track Environmental Restoration # <p>Sometimes scientists conduct a census, too! Learn how population sampling can help monitor the progress of an ecological restoration project.</p>
Perspectives Video: Teaching Ideas
- Pitfall Trap Classroom Activity # <p>Patrick Milligan shares a teaching idea for collecting insect samples.</p>
- Collecting Population Data: "What Lives in the Wetland?" # Want an unforgettable field trip led by a real scientist where your students get hands-on experience with collecting population data? Consider the "" educational program from Remote Footprints. Download the CPALMS Perspectives video student note taking guide.
Problem-Solving Tasks
- Mr. Brigg's Class Likes Math # In a poll of Mr. Briggs's math class, 67% of the students say that math is their favorite academic subject. The editor of the school paper is in the class, and he wants to write an article for the paper saying that math is the most popular subject at the school. Explain why this is not a valid conclusion and suggest a way to gather better data to determine what subject is most popular.
- Election Poll, Variation 1 # This task introduces the fundamental statistical ideas of using data summaries (statistics) from random samples to draw inferences (reasoned conclusions) about population characteristics (parameters). There are two important goals in this task: seeing the need for random sampling and using randomization to investigate the behavior of a sample statistic. These introduce the basic ideas of statistical inference and can be accomplished with minimal knowledge of probability.
Text Resource
- Cell Phone Ownership Hits 91% of Adults # This informational text resource is intended to support reading in the content area. A Pew Research Center survey indicates that cell phone ownership is at an all-time high, with 91% of Americans owning a cell phone in 2013. Statistical tests show that cell phone usage is significantly higher in men, college-educated people, the wealthy, and those living in urban/suburban areas. This rise in ownership is associated with a variety of positive impacts of cell phone use, but previous research shows there are several negative impressions and impacts of cell phones as well.
MFAS Formative Assessments
- Movie Genre # Students are asked to use data from a random sample to draw an inference about a population.
- Prediction Predicament # Students are asked to use sample data to make and assess a prediction.
- School Days # Students are asked to use data from a random sample to estimate a population parameter and explain what might be done to increase confidence in the estimate.