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Understand how statistics can be used to quantify the probability that data support statistical hypotheses.

Design experimental replicates that receive equivalent but independently applied experimental conditions.

Conduct independent t-tests (using Excel) to analyze data.

Understand how sample size affects the ability to detect a significant effect when there is variation in the system (signal-to-noise ratio).

Write research hypotheses that explicitly state the variables measured.

Assign experimental treatments systematically if a known factor (other than the independent variable) influences the dependent variable.

Consider how well an experiment tests the direct effects of an independent variable.

Recognize when paired t-tests, independent t-tests, and ANOVAs are useful.

Student research papers demonstrate appropriate use and presentation of statistical tools in the methods and results sections.

Students indicate that they plan to take additional statistics courses.

Students have the opportunity to formulate and test their own hypotheses.

Students consider how to collect and interpret real data.

Students engage in discussions about challenges of realistic experimental design.

Students discuss pros and cons of different statistical tools, including arbitrary procedures followed by most scientists.

Small groups investigate more challenging questions through the use of graphical models and statistical tests.

Students and instructors evaluate learning gains at multiple time points.

Students participate in the learning process through a variety of teaching modes: independent worksheets, small group projects, class discussions, instructing peers, interactive chalkboard lecture, moving around the classroom to vote with stickers, and answering a variety of questions.

Sample research scenarios include a wide variety of biological sub-disciplines. [ Note : We would also suggest using fictional researcher names (instead of generic “the researcher”) that may identify gender or ethnicity. Alternatively, describing real research scenarios would be ideal.].

Instructor asks questions that include several correct answers, and discusses the merits of each student's verbal contributions.

Students are empowered to make unique choices in selecting their own research questions.

Students are assigned to research groups, so they have the chance to practice working with diverse individuals.

Following completion of a homework assignment, students discuss challenging questions in small groups and then as a whole class.

A brainstorming activity encourages many correct answers about the pros and cons of different experimental designs.

In an immediate feedback question (such as ‘clicker questions') modified for a smaller group, students select their answers with color-coded stickers.

Using a modified ‘jigsaw' teaching approach, student groups are each responsible for investigating and teaching a different topic to the rest of the class (using overhead transparencies).

Small groups design and complete their own small research projects.

Students receive copies of learning goals and a sample research paper when instructors discussed what would be expected of student research papers.

(formative) Sticker voting, small group discussions, and whole-class discussions reveal student understanding for both students and instructors.

(formative) Two homework assignments indicate how well students understood reading assignments.

(formative) An initial survey conducted before the teaching unit helps instructors anticipate challenging aspects of the unit.

(formative) Instructors conduct a ‘pilot study' with students outside of the class to assess effectiveness and clarity of the survey and homework assignment.

(summative) A final survey that parallels the initial survey allows instructors to test learning gains and assess self-reported changes in confidence.

(summative) Final research papers reveal how accurately students applied the new concepts to their own research projects.