Scientific Inquiry & Reasoning Skills - Skill 4: Data-based Statistical Reasoning
These questions will ask you to demonstrate that you can identify patterns in data and draw conclusions from evidence.
Questions that test this skill may ask you to demonstrate your knowledge of the ways natural, behavioral and social scientists use measures of central tendency and dispersion to describe their data. These questions may ask you to demonstrate your understanding of the ways scientists think about random and systematic errors in their experiments and datasets. They may also ask you to demonstrate your understanding of how scientists think about uncertainty and the implications of uncertainty for statistical testing and the inferences they can draw from their data. These questions may ask you to show how scientists use data to make comparisons between variables or explain relationships between them or make predictions. They may ask you to use data to answer research questions or draw conclusions.
These questions may ask you to demonstrate your knowledge of the ways scientists draw inferences from their results about associations between variables or causal relationships between them. Questions that test this skill may ask you to examine evidence from a scientific study and point out statements that go beyond the evidence. Or they may ask you to suggest alternative explanations for the same data.
Questions that test this skill will ask you to use your knowledge of data-based and statistical reasoning by, for example,
- Using, analyzing, and interpreting data in figures, graphs, and tables
- Evaluating whether representations make sense for particular scientific observations and data
- Using measures of central tendency (mean, median, and mode) and measures of dispersion (range, inter-quartile range, and standard deviation) to describe data
- Reasoning about random and systematic error
- Reasoning about statistical significance and uncertainty (e.g., interpreting statistical significance levels, interpreting a confidence interval)
- Using data to explain relationships between variables or make predictions
- Using data to answer research questions and draw conclusions
- Identifying conclusions that are supported by research results
- Determining the implications of results for real-world situations
- Identifying the correlation between a demographic variable, such as race/ethnicity, gender, or age, with life expectancy or another health outcome
- Identifying the relationship between demographic variables and health variables reported in a table or figure
- Explaining why income data are usually reported using the median rather than the mean
- Reasoning about what inference is supported by a table of correlations between different socioeconomic variables and level of participation in different physical activities
- Reasoning about the type of comparisons made in an experimental study of cognitive dissonance and what the findings imply for attitude and behavior change
- Drawing conclusions about the type of memory affected by an experimental manipulation when you are shown a graph of findings from a memory experiment
- Distinguishing the kinds of claims that can be made when using longitudinal data, cross-sectional data, or experimental data in studies of social interaction
- Identifying which conclusion about mathematical understanding in young children is supported by time data reported in a developmental study
- Evaluating data collected from different types of research studies, such as comparing results from a qualitative study of mechanisms for coping with stress with results from a quantitative study of social support networks
- Using data, such as interviews with cancer patients or a national survey of health behaviors, to determine a practical application based on a study’s results