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
Outliers
t-test for outliers
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