Review Statistic Please Answer The Question – Assignment Example

Review statistics: Response to questions Significance of having at least interval level data Interval level of data is desirable because of its equidistance property that facilitates analysis. It is particularly important in inferential analysis in which difference between data values is the center of focus (Gravetter and Wallnau, 2011).
Reasons for preference of the mean as a measure for central tendency
One of the reasons for which the mean is preferred as a measure of central tendency is its incorporation of all data to be represented that meets the need of a central measure. This is contrary to other measures of central tendency that do not consider magnitudes of every data in a data set. The mean is also preferred because of its close relation to measures of dispersion such as standard deviation (Gravetter and Wallnau, 2011).
Reasons for instability of the range as a measure of variability
The range is an unstable measure of variability because it only considers the minimum and the maximum values. This defines its suitability to measuring variability of the extremes but not the other values in between (Wood and Haber, 2013; Gravetter and Wallnau, 2011).
Intended descriptions of measures of variability
Measures of dispersion are intended for describing variation in a data set, describing reliability of the mean, and describing difference in trend between two or more data sets through exploration of variability in distribution of data (Wood and Haber, 2013; Gravetter and Wallnau, 2011).
Factors to consider when determining level of significance in hypothesis testing
Important factors to consider when determining level of significance is the standard error, sample size, variance, and the nature of the test, whether it is a one tailed test or a two tailed test, because the factors have direct effects on significance of a test (Wood and Haber, 2013; Gravetter and Wallnau, 2011).
Reference
Gravetter, F. and Wallnau, L. (2011). Essentials of statistics for the behavioural science. Belmont, CA: Cengage Learning.
Wood, G. and Haber, J. (2013). Nursing research: Methods and critical appraisal of evidence-based practice. Boston, MA: Elsevier Science Health Science Division.