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I. O. Agada, C. A. Oguche and P. O. Agada

**keywords:** Cadmium, caffeine, Cola nitida, contaminants, farmers, pollutants

This study aims at analyzing the variability in some climatic parameters in Oyo Nigeria, with the implication of selecting the right descriptive statistics. 34 years’ data (1977-2010) on daily Rainfall amount, maximum and minimum Relative-Humidity, Sunshine hour, Solar irradiance, Evaporation, maximum and minimum Air temperature, and Wind speed were sourced from the International Institute of Tropical Agriculture (IITA) and used in the analysis. The descriptive statistics employed are: Mean, Standard Deviation and Coefficient of Variation. The One-way Analysis of Variance (ANOVA) was employed in checking whether the variability in the climatic parameters is affected by season. The cluster analysis main goal is to divide the climatic parameters into consistent and distinct groups. The study revealed that the mean is a good approximation of the data set of minimum and maximum Air temperature and maximum Relative humidity because the coefficient of variation is small (<20%). Higher values of coefficient of variation (>20%) as observed in rainfall, evaporation, wind speed and sunshine hours indicate higher uncertainty in the use of the mean as an approximation of the center of the data set. The study recommends that measures of dispersion (range, standard deviation and/or variance) be used in addition to the mean in describing the data. The study further revealed that the variability in solar irradiance affects the variability in Evaporation and Sunshine hours, while the variability in rainfall amounts affects the variability in the minimum and maximum Relative humidity and minimum Air temperature. Lastly the variability in maximum Air temperature affects that of Wind speed. The monthly mean of each of the climatic parameters in the month of May and November are very far apart (no similarity) as revealed by the cluster analysis, because it has the highest squared Euclidean distance of 715.612, and 3.997 between the month of June and October which is the shortest distance. This means that the monthly mean of each of the climatic parameters in the month of June and October are so close (little or no difference). Consequently, the researchers opined that it is reasonable to compute monthly descriptive statistics instead of yearly because, computing yearly descriptive statistics collapse seasonal effects as revealed in the result of the One-way Analysis of variance (ANOVA). The study concluded that the use of the value of the coefficient of variation (<20% or >20%) should inform the selection of the mean as a measure of central tendency and that seasonal effects on the measurements should be checked to avoid the collapse of season when there is indeed significant difference in the monthly mean of the climatic parameter.

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