Definition of Data Interpretation


Data interpretation refers to the process of critiquing and determining the significance of important information, such as survey results, experimental findings, observations or narrative reports. Interpreting data is an important critical thinking skill that helps you comprehend text books, graphs and tables. Researchers use a similar but more meticulous process to gather, analyze and interpret data. Experimental scientists base their interpretations largely on objective data and statistical calculations. Social scientists interpret the results of written reports that are rich in descriptive detail but may be devoid of mathematical calculations.

Quantitative Interpretation

  • Scientists interpret the results of rigorous experiments that are performed under specific conditions. Quantifiable data are entered into spreadsheets and statistical software programs, and then interpreted by researchers seeking to determine if the results they achieved are statistically significant or more likely due to chance or error. The results help prove or disprove hypotheses generated from an existing theory. By using scientific methods, researchers can generalize about how their results might apply to a larger population. For example, if data show that a small group of cancer patients in a voluntary drug study went into remission after taking a new drug, other cancer patients might also benefit from it.

Qualitative Interpretation

  • Certain academic disciplines, such as sociology, anthropology and women’s studies, rely heavily on the collection and interpretation of qualitative data. Researchers seek new knowledge and insight into phenomena such as the stages of grief following a loss, for example. Instead of controlled experiments, data is collected through techniques such as field observations or personal interviews of research subjects that are recorded and transcribed. Social scientists study field notes or look for themes in transcriptions to make meaning out of the data.

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