Summary In market research, the method used to analyze any given piece of information is as weighty as the method used to gather data. Failure to use the right data collection method would mislead a researcher into assembling irrelevant data. Likewise, using the wrong data analysis method would result into wrong results and unwarranted conclusions even in situations where a researcher has the right data (Diamantopoulos 77). This article is a presentation on factors that usually influence a researcher’s choice of statistical analysis method to apply in analyzing a set of data (Diamantopoulos 87). The paper presents pertinent considerations that guide a researcher’s choice between parametric and non-parametric analysis techniques. Key learning points Diamantopoulos’ article has the following key learning points:  1. A market researcher should not leave the selection of the appropriate analytical method to his or her computer software, instead      1. The researcher should carefully consider his or her analysis objectives, that is, what s/he is attempting to accomplish      2. The researcher should think carefully about the nature of data s/he is working with; that is, data characteristics  2. The computer is a device that makes work efficient and can do only what it is instructed to do. Bear in mind that the reliability and accuracy of the results it produces rely on the quality of data fed into it and expertise with which it is instructed to analyze it (Diamantopoulos 78). Critical analysis Setting analysis objectives is a decisive step in the analysis process because it lays out the path towards getting the correct results which forms the basis for making relevant conclusions that are a true picture of the reality (Zikmund and Babin 68). A focused researcher should bear in mind the three main roles that coming up with clear analysis objectives play. This includes ensuring that the researcher undertakes only relevant analysis (Diamantopoulos 78; McDaniel and Gates 18). It is pertinent to note that if the analysis undertaken does not contribute directly towards answering the research topics of interest, then it is not relevant. Secondly, setting analysis objectives enables the researcher to ensure comprehensiveness of the process (Diamantopoulos 78). The Depth of the analysis process ensures that the researcher has fully used the information potential in a set of data. In other words, analyzing data on just some variables makes your analysis incomprehensive even though it may be relevant. Third, setting analysis objectives enables a researcher to shun redundancy (Diamantopoulos 78). Having analysis objectives ensures that, the only relevant thing is done, and it is done adequately without repetition (Diamantopoulos 78). This can be likened to having a clear roadmap with signposts that warn a traveller against dangerous and unnecessarily long routes as s/he moves toward his or her destiny. Different parts of a research study should be properly related because; each part determines how successful the researcher is, in getting correct results that are a true reflection of reality. For instance, the research objectives form the point of departure for coming up with analysis objectives. Therefore, the link between research objectives and the analysis objectives should be clear (Diamantopoulos 79; Hair, Bush, and Ortinau 49). This means that developin