Statistical tools are undoubtedly important in decision making. The use of these tools in everyday problems has led to a number of discoveries, conclusions and enhancement of knowledge. This ranges from direct calculations using general statistical formulas to formulas integrated in Statistical software to fasten the process of decision making. Statistical tools for testing hypothesis, significance tests are strong but only if used correctly and in good understanding of their concepts and limitations. Some researchers have indulged into wrong usage of this tests leading to wrong conclusions. This paper looks at the different significance tests (both parametric and non-parametric tests) their uses, when to be used and their limitations. It also evaluates the use of Statistical Significance tests in Information Retrieval and then proceeds to check the different significant tests used by researchers in the papers submitted to Special Interest Group on Information Retrieval (SIGR) in the period 2006, 2007 and 2008. For the combined period 2006-2008, including the years 2006 and 2008, of the papers submitted had statistical tests used and of these tests were used wrongly. Key Words: Significance Test, Information Retrieval, Parametric Tests, Non-parametric Tests, Hypothesis Testing
Statistical methods play a very important role in all aspects of research, ranging from data collection, recording, analysis, to making conclusions and inferences. The credibility of the research results and conclusions will depend on each and every step mentioned above; any fault made in these steps can render a research carried out for several years, spending millions of shillings to be worthless. This does not mean carrying any test and mincing figures shows that statistics has been used in the given research; the researcher should be able support why he or she used that specific test or method. Misuse of significance test is not new in the world of science. According to Campbell (1974), there are different types of statistical misuse:
This occurs when the researcher selects only a portion of data which produces the results that he/she requires perfectly while discarding the other portion. After a well done research, the researcher might get values that are not consistent to what he/she was expecting. This researcher might decide to ignore this section of data during the analysis so as to get the “expected results”. This is a wrong take since the inconsistent data could give very new thoughts in that particular field that is if these irregularities are checked and explained why they occurred, more ideas abut that area can be explored..
Sometimes the conclusions from a research can only work on that particular research problem but the researcher might blindly generalize the results obtained to other kinds of research similar or dissimilar. Overgeneralization is a common mistake in current research activities. A researcher after successfully completing a research on a particular field, he/she might be tempted to make generalizations reached in this research to other fields of study without regarding the different orientations of these different populations and assumptions in them.
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