What is CABS?

This site will help high school students and teachers find original, independent science research topics and questions that can be done without a professional lab...these can be done in a school lab or even in one's basement! The project ideas and research questions being developed and presented here have been vetted and could lead to true discoveries, and not just finding already known results. See our Welcome message. These are the types of projects that could be done and submitted to high school contests such as the Regeneron Science Talent Search, Junior Science and Humanities Symposium, or the Intel International Science and Engineering Fair, and be competitive. If you have an idea to share, or a question about one of the project ideas, contact us at vondracekm@eths202.org.

Pages (on the right side of the screen) have lists of ideas for different types of science research projects, and clicking on one of those ideas will take you to posts with details and all sorts of information about that type of project. Get more information about why there is a need for CABS!

Data Analysis Resources

Regardless of what type of project a researcher takes on (experimental, theoretical/computational, online data sets), once data are collected one must analyze the data. This can take a variety of forms, and often depends on the measurements being made. This page will briefly define a number of analysis 'tools' and techniques, and have links to more extensive posts that will include explanations, examples, videos, techniques, and so on, related to the analysis process.

The Analysis part of any research project is the process of getting results and information from data and observations that will allow the researcher to make any conclusions from the experiment or simulation.

Some basic statistics used in most research projects
Measurement trials
Average or mean
Standard deviation
Chi-square test: this is a way of determining how close your data are to expected results. If, for
                          instance, you are testing a theoretical prediction, you can begin to determine how                                 believable the prediction - are any discrepancies between measurement and the                                     predicted due to statistical fluctuations, or is there a real problem with the prediction?

Some basic graphing terms and techniques
Scatter plot
Error bar
Curve fitting
Best-fit function
t-test for outliers

No comments:

Post a Comment