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During researches, results can be statistically significant but not meaningful. The situations occurs at the end of a study when the statistical figures relating to certain topics of study are calculated in absence of qualitative aspect and other details that can be used to make decisions (Munro, 2005). In this scenario, one cannot make an appropriate decision leading to misguided course of action.
Therefore, a decision should not only depend on the statistical figures but also on the involved parameters, which may not be indicated in the results of the study. Normally, the study results are analyzed using various statistical mechanisms available. These include the use of pie charts, line graphs, and bar graphs.
By looking into the results that has been tabulated, you can make conclusions by merely approximating the statistical significance. The situation is evidenced when the lack of sufficient sample is experienced during the study. For instance, per capita income of a country may be statistically significant yet it has no meaning.
In such situations, the distribution of income, living standards, and inflation within a given country will not be indicated. Under such conditions, the results of a study can be statistically significant despite lacking means to those making the decisions. If such results are presented to organizations or government ministries, they will not make much impact during decision-making process.
Example: Health statistics
In health statistics, the significance of the results of a particular research is its quality and the medical significance attached to its results (Munro, 2005). In the field of medicine, studies and researches are carried on a chosen sample of the population to represent the entire population. The problem arises when a study is carried out on certain people as the medication is targeted on another different population.
The above leads to inappropriate decision-making because the other population may not be equivalent to the sampled portion of the population. Another factor of concern is the size of the population to be sampled during the study as it has great influence on the credibility of the results. Preconceived notion to a greater extend undermines the outcome of a study and all these affects the statistical significance of results for a particular research.
The statistical significance mainly deals with the computation of the probability of the results of a given study being due to chance. The statistical significance is usually expressed as a probability. The letter ‘P’ is used to denote probability and conventionally is taken to be at 5%, that is up<0.05. The results imply that there exists 5% tendency of occurrence of results by chance (Munro, 2005). Usually, the results of any study are termed to be statistically significant if there is a small probability of compatibility between it and the null hypothesis.
The reasoning here is that carrying out a medical study on a sample of a given geographical location and applying the same results on another different population can be misleading because the effect could be due to chance. It only becomes significant if there is repeatability of results.
Clinical significance states that a small improvement in the new medication should not be sufficient to change the medication to the new method. Clinical significance requires that thorough analysis of the benefits, cost, disadvantages, technology, and the patient preference about the new medication. The above is important before adopting the new medication.
In critical areas of life, the statistical significance only should not be used to make the decisions concerning the topic of study (Salkind, 2014). As such, the results can have statistical importance but does not portray the quality and clear details of the results of a particular study.
Munro, B. H. (2005). Statistical methods for health care research (5th ed.). Philadelphia: Lippincott Williams & Wilkins.
Salkind, N. J. (2014). Exploring research (8th ed.).Harlow, Essex: Pearson Education.