Use random sampling to draw inferences about a population | Random sampling means picking a group by chance so the results represent everyone fairly. Students use that sample to make reasonable guesses about a larger group, like estimating how many kids in a school prefer a certain lunch. | 7.SP.1 |
Understand that statistics can be used to gain information about a population… | Surveying part of a group to draw conclusions about the whole group only works if that smaller group fairly represents everyone. Random sampling is the most reliable way to get a fair slice. | M.7.17 |
Use data from a random sample to draw inferences about a population with an… | Students pick a small random sample from a larger group, use it to make a prediction about the whole group, then repeat the process a few times to see how much their estimates shift. | M.7.18 |
Draw informal comparative inferences about two populations | Students look at data from two groups and draw conclusions about how they compare. For example, they might decide whether one class reads more books per month than another based on a dot plot or histogram. | 7.SP.2 |
Given two data displays, distinguish measures of center and measures of… | Students compare two sets of data and identify which group has a higher average and which group's numbers are more spread out. They use both pieces of information together to draw conclusions about the two groups. | M.7.19 |
Compare two numerical data sets in relation to their context, such as by | Reading two sets of data side by side, students compare things like test scores or heights across two groups. They look at how the numbers spread out and where the middle falls to draw conclusions about what the data actually shows. | M.7.20 |
Reporting the number of observations | Students count how many data points are in each group before comparing the two groups. A larger sample usually gives a more reliable picture. | M.7.20.a |
Describing the nature of the attribute under investigation, including how it… | Students explain what a data set is actually measuring, such as height in inches or time in minutes, and describe how that measurement was collected. This gives context to any comparison made between two groups. | M.7.20.b |
Giving quantitative measures of center | Students summarize data from two groups by finding the median or mean of each, then describe what those numbers reveal about the overall pattern. This helps compare the two groups side by side. | M.7.20.c |
Giving quantitative measures of variability | Students compare two data sets by measuring how spread out the numbers are, using tools like the range or interquartile range. They also call out anything unusual that doesn't fit the pattern. | M.7.20.d |
Relating the choice of measures of center and variability to the shape of the… | Students learn when to use the mean or median to describe a data set and when to use range or spread, based on whether the data is balanced or skewed and what the numbers actually represent. | M.7.20.e |
Informally assess the degree of visual overlap of two numerical data… | Students compare two sets of data on a dot plot and judge how far apart the groups are. They measure the gap between the two averages and describe it in terms of how spread out each group's data is. | M.7.21 |
Use measures of center and measures of variability for numerical data from… | Students compare two groups using averages and spread, like checking whether words in a seventh-grade textbook are typically longer than words in a fourth-grade one. The goal is to draw a reasonable conclusion from real data, not just guess. | M.7.22 |
Investigate chance processes and develop, use | Students compare two sets of data by looking at how spread out each set is, not just which average is higher. They use that spread to draw conclusions about which group performed differently. | 7.SP.3 |
Understand that the probability of a chance event is a number between 0 and 1… | Probability is a number from 0 to 1 that shows how likely something is to happen. Close to 0 means it probably won't happen, close to 1 means it probably will, and around 0.5 means it's a coin flip. | M.7.23 |
Approximate the probability of a chance event by collecting data on the chance… | Students roll a die or flip a coin many times, record what happens, and use those results to predict how often an outcome will occur in a large number of tries. The more trials they run, the closer the results get to the true probability. | M.7.24 |
Develop a probability model and use it to find probabilities of events | Students build a simple probability model (like predicting how often a coin lands heads), then run the experiment and compare what they predicted to what actually happened. If the numbers don't match, they explain why. | M.7.25 |
Develop a uniform probability model by assigning equal probability to all… | When every outcome has the same chance of happening, students calculate the probability of a specific result. For example, if names are drawn from a hat, they find the fraction that represents one name or a whole group being picked. | M.7.25.a |
Develop a probability model | Students collect real results from an experiment, like spinning a penny or tossing a cup, then use those results to estimate how likely each outcome is. The probability comes from what actually happened, not from assuming every outcome has an equal chance. | M.7.25.b |
Find probabilities of compound events using organized lists, tables, tree… | Students figure out the chances of two or more things happening together, like flipping a coin and rolling a die at the same time. They use lists, tables, or diagrams to map out every possible outcome. | M.7.26 |
Understand that, just as with simple events, the probability of a compound… | Compound events combine two or more simple events, like flipping a coin and rolling a die together. Students find the probability by counting how many outcomes in the full list of possibilities match what they're looking for, then writing that as a fraction. | M.7.26.a |
Represent sample spaces for compound events using methods such as organized… | Students list every possible outcome of two combined events (like rolling two dice) using a table, chart, or branching diagram, then circle the specific outcomes that match a given result. | M.7.26.b |
Design and use a simulation to generate frequencies for compound events | Students set up a simple experiment, like drawing cards or using a number generator, to estimate how often two or more chance events happen together. Then they use the results to answer real probability questions. | M.7.26.c |