Statistics and Their Importance to Research Investigation. Although all research activities do not require the use of statistical data analysis when an investigator wants to report upon the differences, effects and/or relationships between and amongst groups or phenomena (i.e. variables) there must a concerted effort to measure the phenomenon with as much precision and accuracy as possible (Mendenhall & Ramey, 1973). This is, of course, accomplished through the use of statistics. Situations wherein statistical processes are not required are generally reserved for qualitative types of research such as historical, philosophical, and/or cultural trend studies.
One of man's greatest fears is that of the power of numbers. For most people numbers are nothing more than a hodgepodge of digits that are bewildering and often meaningless. As a result individuals often prefer to judge events, occurrences, phenomena, and situations from a traditionalist point-of-view wherein reason, conclusion, and inference are made on the basis of past acceptance rather than on a best practices policy. Justification for historical acceptance is usually based upon a fear of numbers and a lack of willingness to extract meaningful information from them. For those accepting of alternatives, statistical tools have been devised wherein it is possible to extract meaningful information from data and interpret whatever the data holds as its' secret (Freund, 1967). However, as test data often comes to the researcher investigator in unintelligible form, as test numbers by themselves are, the researcher must be knowledgeable enough to be able to choose the most reliable and efficient best-fit practice statistical procedure. If the appropriate procedure is not chosen the researcher will then wander through a labyrinth of unimportant data and embark upon an odyssey of failure (Ohlson 1998).
Before one enters the world of statistics one must put away all their fears and illusions about statistics. The stimuli that incite mathematical panic are largely illusory. Many of the formulas used by scientists in their statistical computations present an awesome, if not terrifying, appearance, but beneath the strange symbols lurks nothing more foreboding than the simple arithmetic we all mastered in school. The uses one will make of best practice statistics require no differential equations, no calculus, and no analytic geometry. The sometimes-horrifying mathematical manipulations that fill one with ghastly anxiety as they approach a lesson in statistics ultimately reveal themselves as addition, subtraction, multiplication, and division. By becoming familiar with the statistical techniques one might possibly develop a benevolent tolerance for the tedium and possibly even a reverent respect for the almost magical things statistics allows one to do with mere numbers.
Correct Statistical Processes. Statistics is a branch of scientific mathematical methodology. It deals with the collection, classification, description, and interpretation of measurement data obtained through the testing process and observation. In consumer product research the essential purpose is to describe and draw inferences about the numerical properties of product populations as well as to compare testing procedures (Senter, 1969). In everyday language the term population is used to refer to groups or aggregates of consumer product. The consumer product researcher's concern is with properties which are descriptive of a group or aggregation itself rather than with properties of particular single product member. As consumer product researchers are primarily concerned with group issues, rather than single product unit matters, it is paramount that a complete familiarization and understanding be had as to the proper use of statistical processes. The investigator must discern when to use a particular statistical process for comparing a single unit to a group opposed to comparing two (2) or more groups to each other, or comparing a group to a wider population. Should the wrong statistical process be employed there will result contaminated statistical values and wrongful conclusions will be drawn. As a consequence of the use of improper statistical processes unsafe consumer product may be accepted; client liability will increase; and corporate profit will diminish.
The primary purpose of statistical processes is to make order out of chaos. By properly applying selected statistical processes to measurement data the consumer product engineer can determine whether or not a manufactured product is safe for distribution. However, product safety is simply a stratagem if the wrong statistical procedure is applied to measurement data. Once the researcher has collected the necessary measurement data it is then time to apply an appropriate statistical tool that will confirm or deny