Important measures in data summarization include measures of central tendency (i.e. "averages" or the mean, median, and/or mode) and measures of dispersion or variability -- the range of the data and the standard deviation of the points within the data set (Shaughnessy et al. 2006). These statistical staring points can be used to derive a wealth of information form the data, including correlations to other related studies/data sets, reliability and consistency of the data set at hand, and other summary statistics that provide the necessary measures to begin to understand the implications of a given data set (Shaughnessy et al. 2006). These basic figures must be known before any statistical analysis can occur.
Effect size is also a very important measure in the summary of a data set (Shaughnessy et al. 2006). Rather than simply showing a correlation between various features of a data set, effect size measures determine the strength of such relationships; some things that appear to be correlative might have effect sizes that are quite small, suggesting perhaps a different causal agent common to the two phenomenon, or leading to questions requiring firther research (Shaughnessy et al. 2006). In this way, even when data analysis points away from desired conclusions, a carefully conducted research project...
Summary provides the clear and concise results of an experiment as represented through the data, and is therefore quite essential to the overall process of data analysis.
Confirming What the Data Reveals
The final stage in data analysis is confirming what the summary and individual data points do, in fact, reveal (Shaughnessy et al. 2006). Confidence intervals, such as the difference between two means, can be used in this stage of data analysis in order to confirm that the research was conducted objectively and that the data remains free from any contamination or bias in analysis (Shaughnessy et al. 2006). It is vitally important not to draw conclusions before this stage; even though certain relationships between the data points might be perceptible, determining these things to early in the process can lead to unintentional bias later on (Shaughnessy et al. 2006). Only after all of the necessary summary points of a data set have been calculated and displayed can the true import of a data set be determined, and even then it is important to proceed with skepticism and scientific caution (Shaughnessy et al. 2006). Confirming the meaning of data is the final step in analysis, but it is only the beginning of applying…
