Chapter 7 introduces statistical power and effect size in hypothesis testing. Guidelines for interpretation of effect size, along with other sources of increasing statistical power, are provided. Point estimation and interval estimation and their relationship to population parameter estimates and the hypothesis-testing process are considered. Statistical significance is highly sensitive to large sample sizes. This means that researchers, in addition to selecting desired statistical significance p-values, need to know the magnitude of the treatment effect or the effect size of the behavior under consideration. Effect size determines sample size, and sample size is intimately related to statistical power or the likelihood of rejecting a false null hypothesis.

This chapter deals with

• The notion of exploratory spatial data analysis

• The presentation of descriptive statistics

• Spatial statistics and their importance in analyzing spatial data

• Analyzing univariate data

• Simple exploratory spatial data analysis tools such as histograms, boxplots and other visual methods to get a better insight of spatial datasets

• Bivariate analysis

• Correlation and pairwise correlation

• Normalization, rescaling and adjustments

• An introduction of basic notions of statistical significant tests

• The importance of hypothesis setting in a spatial context

• The importance of normal distribution in classic statistics and how it is integrated into spatial analysis

After a thorough study of the theory and lab sections, you will be able to

• Have a solid knowledge of descriptive statistics

• Use descriptive statistics for univariate analysis

• Understand and use exploratory spatial data analysis techniques to map and analyze variables attached to spatial objects

• Create several plots, link them to maps and identify interesting patterns of data

• Conduct bivariate analysis, identify whether two variables are linearly related and use plots to further examine their relation

• Rescale data to make comparisons between variables easier and also allow for better data handling

• Apply ESDA tools through ArcGIS and GeoDa