Understand the distinction between mathematical models and statistical models High School | Mathematical models use exact rules to predict outcomes. Statistical models use patterns in real data to make predictions, knowing some uncertainty is always part of the answer. | S.Q.1 |
Distinguish among different sources of variability, including measurement… High School | Students learn that data varies for different reasons: a ruler might be read slightly wrong, people naturally differ, an experiment might introduce a change, or a sample might not reflect the whole group. Recognizing the source of that variation matters. | S.Q.1.1 |
Formulate meaningful statistical questions to clarify the problem at hand High School | Students practice turning a vague problem into a clear question that data can actually answer. A good statistical question leaves room for different answers, so "How tall are students in this school?" works, but "How tall am I?" does not. | S.Q.1.2 |
Distinguish between the distribution of a population, a distribution of sample… High School | Students learn to tell apart three different kinds of data pictures: what an entire group looks like, what one survey sample shows, and what happens when you repeat that sampling many times. Each one answers a different question. | S.Q.2 |
Distinguish between sample statistics and population parameters High School | Students learn the difference between a number that describes a small group you actually measured and a number that describes the entire group you care about. A survey of 200 voters is a sample; all registered voters in the state is the population. | S.Q.2.1 |
Recognize a population distribution has fixed values of its parameters and that… High School | A population parameter (like the average height of all adults in a country) is a single fixed number. In practice, we rarely know that exact number, so statisticians estimate it from a sample. | S.Q.2.2 |
Recognize that a sample data distribution is taken from a population… High School | A sample is a slice of a larger group. When students collect or read data, they're seeing that slice, not the whole population, and understanding the difference shapes how they interpret any result. | S.Q.2.3 |
Recognize a sampling distribution is the distribution of a sample statistic High School | A sampling distribution shows what happens when you take the same statistic (like an average or percentage) from many different random samples. The results form a pattern, and that pattern is what statisticians study to make predictions about a larger group. | S.Q.2.4 |
Identify differences between categorical and quantitative data High School | Categorical data sorts things into named groups, like favorite colors or pizza toppings. Quantitative data uses numbers you can measure or count, like height or test scores. Students learn to tell the two apart. | S.Q.3 |
Determine whether categorical or quantitative data is appropriate to answer a… High School | Students look at a statistical question and decide what kind of data answers it: category labels (like favorite sport or blood type) or numbers you can measure (like height or test scores). | S.Q.3.1 |
Compare and contrast different potential graphical or visual representations… High School | Students look at the same set of data displayed in different chart types (bar graph, pie chart, line graph) and decide which one makes the pattern clearest and why. | S.Q.3.2 |
Distinguish among different types of study designs for collecting data High School | Students learn the difference between surveys, experiments, and observational studies, then decide what kind of conclusion each one can actually support. A survey can describe a group; only a well-designed experiment can show cause and effect. | S.DC.1 |
Distinguish among sample surveys, experiments High School | Students learn the difference between asking people questions (a survey), watching what happens without interfering (an observational study), and testing what happens when you change one thing on purpose (an experiment). | S.DC.1.1 |
Compare and contrast the benefits of different sampling techniques High School | Students learn why some ways of choosing survey participants give more reliable results than others. They compare methods like random sampling and convenience sampling to decide which one fits a situation best. | S.DC.1.2 |
Determine the appropriate scope of inference for generalizing results High School | Students learn when it's fair to apply findings from a small group to a larger population. That means asking whether the sample was random and who, exactly, it can actually represent. | S.DC.1.3 |
Explain how sample size impacts the precision with which generalizations can be… High School | Bigger samples give more reliable results. Students learn why a survey of 1,000 people tells you more than a survey of 10, and how the size of a sample determines how confidently you can draw conclusions about a larger group. | S.DC.1.4 |
Determine when a cause-and-effect inference can be drawn from an association… High School | Students learn when it's fair to say one thing *caused* another, versus when two things just happen to move together. The key is how the data were collected: a controlled experiment can support cause-and-effect; a survey or observation usually cannot. | S.DC.1.5 |
Identify common sources of bias and the role of randomization in study design High School | Students learn to spot what can skew a study's results, such as a survey that only reaches certain people, and why randomly selecting participants helps produce findings you can trust. | S.DC.2 |
Explain how randomization and sources of bias impact the results of a study High School | Students learn why random sampling matters and how a biased survey can skew results. The goal is to spot problems in how data was collected before trusting what the numbers say. | S.DC.2.1 |
Understand the different roles of random selection and random assignment in… High School | Random selection picks who is included in a study; random assignment decides who gets which treatment. Knowing the difference tells students whether a study can show cause and effect or just a pattern. | S.DC.2.2 |
Use distributions of quantitative and categorical data to identify the key… High School | Students look at a graph or table of real data and describe what stands out: the shape, the center, any outliers, and what those patterns mean for the actual situation being studied. | S.DA.1 |
Summarize and represent the distribution for univariate quantitative data by… High School | Students look at a single set of numbers, such as test scores or heights, and describe what the data shows: where values cluster, how spread out they are, and whether any values sit far outside the rest. | S.DA.1.1 |
Select and create an appropriate display High School | Students choose the right type of chart for a single set of data and build it. A dot plot works for small data sets, a histogram for large ones, and a box plot to show spread and middle values. | S.DA.1.2 |
Use statistics appropriate to the shape of the data distribution to compare… High School | Students look at two or more sets of data and choose the right summary numbers to compare them. If the data skews or has outliers, they use median and range. If it spreads evenly, they use mean and standard deviation. | S.DA.1.3 |
Describe and analyze the distribution of univariate categorical data High School | Students look at one category of data (like favorite lunch choices or survey responses) and describe the pattern: which answer shows up most, which shows up least, and what the spread tells you. | S.DA.1.4 |
Use the mean and standard deviation of a data set to fit it to a normal… High School | Students use the average and spread of a dataset to match it to a bell curve, then estimate what percentage of a population falls above, below, or between certain values. | S.DA.2 |
Use calculators, computers High School | Students use a calculator or table to find the percentage of data that falls within a range on a bell-shaped curve. They also learn to recognize when that method doesn't fit the data they have. | S.DA.2.1 |
Compare two or more groups by analyzing distributions High School | Students look at two or more sets of data side by side and describe how they differ, noting where values cluster, how spread out they are, and which group tends to score higher or lower. | S.DA.3 |
Construct appropriate parallel graphical displays of distributions High School | Students build side-by-side graphs, such as box plots or histograms, to compare how two or more data sets are spread out and where their values tend to land. | S.DA.3.1 |
Use numerical attributes of distributions to make comparisons between… High School | Students compare two data sets by looking at their averages, spreads, and typical values to explain which group scored higher, varied more, or clustered differently. | S.DA.3.2 |
Analyze associations between two variables High School | Students look at two sets of data side by side to see if a pattern exists between them. For example, they might check whether students who sleep more tend to score higher on tests. | S.DA.4 |
Create two-way tables for bivariate categorical data and analyze for possible… High School | Students sort two sets of category data (like grade level and favorite subject) into a grid, then study the row totals, column totals, and individual cells to see whether the two categories are connected. | S.DA.4.1 |
Make predictions and draw conclusions from regression models High School | Students use a trend line fitted to a scatter plot to make predictions about real-world situations. They work with straight-line, curved, and exponential patterns to decide what a graph suggests will happen next. | S.DA.4.2 |
Analyze scatter plots for patterns, linearity, outliers High School | Students look at a scatter plot and describe what they see: whether the dots form a line, whether the trend goes up or down, and whether any points sit far from the rest or pull the line in a different direction. | S.DA.4.3 |
Using technology, compute and interpret the correlation coefficient High School | Students use a calculator or software to find the correlation coefficient, a number between -1 and 1 that shows how closely two sets of data follow a straight-line pattern. | S.DA.4.4 |
Understand the implications of extrapolating data to make predictions High School | Extrapolating means using a graph or data set to predict values beyond what was actually measured. Students learn why those predictions get less reliable the further out you go, and how to spot when a trend line is being stretched too far. | S.DA.4.5 |
Make statistical inferences and evaluate claims from studies High School | Students look at data from surveys or experiments and decide whether the conclusions hold up. They practice spotting claims that overreach what the numbers actually show. | S.DA.5 |
Construct and interpret confidence intervals for the mean of a normally… High School | Students build a range of values around a survey result or average, then explain what that range means: if the same study ran many times, most of those ranges would contain the true answer. Used with poll results, test scores, and similar data. | S.DA.5.1 |
Explain how a sample statistic and a confidence level are used in the… High School | A confidence interval gives a range of values likely to contain the true population number. Students learn how a sample statistic sets the center of that range and how the confidence level controls how wide the range has to be. | S.DA.5.2 |
Explain how changes in the sample size, confidence level High School | Students learn how survey results get more or less precise depending on how many people were surveyed and how confident you want to be in the answer. A bigger sample shrinks the margin of error; a higher confidence level widens it. | S.DA.5.3 |
Construct a confidence interval for the mean of a normally distributed… High School | Students build a range of likely values around a survey result or average to judge whether a claim about a population is believable. They learn how much wiggle room the data allows before trusting a headline or conclusion. | S.DA.5.4 |
Use confidence intervals to evaluate claims for a single population parameter High School | Students use a range of likely values, calculated from sample data, to decide whether a claim about an entire population holds up. If the claimed value falls outside that range, the claim is probably wrong. | S.DA.5.5 |
Interpret and communicate the results of a statistical analysis in context High School | Students take the numbers from a statistical study and explain what they actually mean in plain terms. That means saying not just what the data shows, but why it matters for the real situation being studied. | S.IR.1 |
Recognize when the difference between two sample proportions or two sample… High School | Students learn to tell whether a difference between two survey results or group averages is a real pattern or just chance variation. This skill is how researchers decide if a finding actually means something. | S.IR.1.1 |
Understand the concept of a confidence interval, including the interpretation… High School | Students learn what it means when a poll says "48% approve, plus or minus 3 points." They practice reading the range of likely true values, judging how certain a result is, and deciding whether a difference between two numbers is real or just chance. | S.IR.1.2 |
Develop inferences or predictions to construct resulting decisions or… High School | Students look at data patterns and make a decision or prediction based on what the numbers actually show. This is the step where math leads to a real conclusion, like recommending a change or forecasting what comes next. | S.IR.1.3 |
Create and evaluate recommendations for areas of future research High School | Students look at what a study found and decide what questions still need answering. They propose next steps a researcher could actually take. | S.IR.1.4 |
Evaluate practical implications of statistical significance or lack thereof High School | Statistical significance sounds like a verdict, but it isn't one. Students learn to ask what a result actually means in the real world, not just whether the math crossed a threshold. | S.IR.2 |
Develop and critique arguments for practical implications based on statistical… High School | Students look at data results and decide whether a finding is big enough to matter in real life, not just in a study. They also push back on other people's conclusions and explain when a statistically significant result might not actually change anything. | S.IR.2.1 |
Identify potential lurking variables which may explain an association between… High School | Students look at a connection between two things (like ice cream sales and drowning rates) and ask whether a hidden third factor is really driving both. Spotting that hidden factor is the skill. | S.IR.2.2 |
Evaluate real-world claims and conclusions High School | Students look at a claim based on data, like a headline or survey result, and decide whether the conclusion actually holds up. This means checking whether the data was collected fairly and whether the numbers support what someone says they prove. | S.IR.3 |
Evaluate strengths and weaknesses in the studies or methods used to generate… High School | Students look at how a study was set up and decide what the results can and can't prove. They spot problems like a biased sample or a flawed question that could make the findings misleading. | S.IR.3.1 |
Evaluate the statistical validity of claims made High School | Students look at a claim backed by data and decide whether the numbers actually support it. They check whether the study was set up fairly and whether the conclusion follows from what was collected. | S.IR.3.2 |
Connect basic probability concepts to statistical analysis High School | Students learn to use probability (the chance that something happens) to make sense of real data. That means moving from "how likely is this?" to "what does this data actually tell us?" | S.P.1 |
Describe events as subsets of a sample space High School | A sample space lists every possible outcome of an experiment, like all sides of a die or all cards in a deck. Students identify specific groups of outcomes within that list, such as all even numbers or all red cards. | S.P.1.1 |
Describe the relationship between theoretical and empirical probabilities using… High School | Theoretical probability is what math predicts should happen. Empirical probability is what actually happens when you run the experiment. The more times students repeat the experiment, the closer the real results get to the prediction. | S.P.1.2 |
Use counting techniques High School | Students use counting methods to figure out how many ways events can happen, then use that to calculate the probability of combined outcomes, like the odds of drawing two specific cards from a deck. | S.P.1.3 |
Determine probabilities, including joint probabilities, conditional… High School | Students figure out the chance that one or more events will happen, including situations where one outcome affects the next. They explain what the numbers actually mean in context. | S.P.2 |
Understand that two events, A and B, are independent if the probability of A… High School | Two events are independent when one outcome has no effect on the other. Students check independence by multiplying the two separate probabilities and comparing the result to the probability of both events happening at the same time. | S.P.2.1 |
Understand and calculate the conditional probability of A given B as P High School | Students find the probability that two things both happen, then divide by the probability of the one they already know occurred. It answers questions like: given that it rained, what are the chances school was canceled? | S.P.2.2 |
Interpret independence of A and B as saying that the conditional probability of… High School | Two events are "independent" when knowing one happened tells you nothing about whether the other will. Students show this by confirming that the probability of A stays the same whether or not B has already occurred. | S.P.2.3 |
Use probability to make decisions High School | Students use probability and expected value to weigh real decisions, like whether a game is worth playing or whether a medical test is worth taking. The math helps evaluate choices, not just calculate odds. | S.P.3 |
Analyze decisions about statistical significance based on reported p-values High School | Students look at a reported p-value and decide whether a study's result is strong enough to trust or likely just random chance. | S.P.3.2 |