This unit offers a review of statistics concepts introduced and reinforced in prior grades. Students begin with an introduction to box-and-whiskers plots as a tool for comparing data sets. In the remaining investigations, students explore what samples are and how they are related to populations, ways to select samples, and the use of random samples. Issues of representation and bias in data analysis are also addressed. Statistics is the science that relies on data to answer questions. A statistical investigation typically encompasses four interrelated components:
posting the question, collecting the data, analyzing the data, and interpreting the results. Students' recognition and use of the process of statistical investigation is important in working with statistics. We continually want to focus their attention on the process even as we work with them to develop strategies that are part of the process, such as computing measures of center or spread and making graphs.

Students will know
· Population (the entire group of interest or study).
· Sample (a subset of a larger population that is representative of the population).
· Sample space (range of values or possible outcomes of a sample).
· Selection (process of selecting a sample from a population).
· Theoretical probability (likelihood of an outcome based on analysis).
· Experimental probability (likelihood of an outcome based on experimentation).
· Average (arithmetic mean).
· Mode (most frequently occurring value or category).
· Median (the middle score or category occurring with the middle frequency).
· Prediction (inferences that can be made about a population from studying a sample of the population).

Students will be able to
· Collecting, organizing, and analyzing data.
· Constructing and interpreting charts, tables, and graphs.
· Developing convincing conclusions or predictions about a population based on careful analysis of a sample of the population.
· Evaluating arguments based on data or predictions/conclusions made from analysis of
data.
· Modeling situations through analysis of data, selection of samples, and caring out experiments (e.g., computing statistics, finding experimental or theoretical probabilities
to measure likelihood of different possible outcomes.
· Developing predictions or conclusions about a sample or population based on experimental or theoretical probabilities.