A data analysis is a comprehensive summary of the results of your research and lists the main conclusions drawn from your tests and experiments. The analysis typically summarizes two main parts of a research document: data preparation and descriptive statistics (experimental studies) and inferential statistics (quantitative studies). The final section is the qualitative analysis that explains why some types of results are relevant.
Define your tests and testing process. Explain all of the tests you performed and why you performed them.
Outline your method for gathering results. Explain how you gathered the data and what measures you took to make sure these were objective and accurate.
Create graphs and charts. Present the results of your tests with a pie chart, scatter graph or other visual aid to illustrate results.
Draw conclusions and make comparisons. Explain what the results mean, why they are important and how they compare to previous studies or similar experiments.
List sources. List any sources you have used within the document at the end of this section.
Describe your sample set. Explain to the reader what the sample set for your study consisted of and why you selected this as your "ideal" for the experiment.
Define the objective of the study. Explain what you want the reader to learn or understand from the study and why this is significant.
Explain the results. Write a few paragraphs that explain the data and results in more detail. This should help the reader see important trends, comparisons and differences.
Confirm or unconfirm the hypothesis. Restate the hypothesis and explain whether the results of your study support it.
Finish with the qualitative analysis. This section is a written summary (no graphs or tables permitted) of your results, and is your opportunity to draw final conclusions and make final comments about your study. This section can be one to two pages long and can be organized with headings and subheadings. This section will also include reasons why results are relevant.