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 Introduction to Biostatistics -statistics are simply a collection of tools that researchers employ to help answer research questions INFERENTIAL STATISTICS: COMMON TESTS Chi-Squire Tests Chi-square test is an inferential statistics technique designed to test for significant relationships between two variables organized in a bivariate table. Chi-square requires no assumptions about the shape of the population distribution from which a sample is drawn. However, like all inferential techniques it assumes random sampling. It can be applied to variables measured at a nominal and/or an ordinal level of measurement. The research hypothesis (H1) proposes that the two variables are related in the population. The null hypothesis (H0) states that no association exists between the two cross-tabulated variables in the population, and therefore the variables are statistically independent. Formula for computing chi-squire statistic Where, O=observed frequency, and  E = expected frequency The essence of the test is to compare the observed frequencies with the frequencies expected for independence if the difference between observed and expected frequencies is large, then we can reject the null hypothesis of independence. Determining the Degrees of Freedom df = (r – 1)(c – 1) where, r = the number of rows and c = the number of columns

 Introduction Definitions Sampling Scales of Measurement Variables Presenting Data Descriptive Statistics Measures of central tendancy Measures of dispersion/variability Normal Distribution and Probability Inferential statistics: Chisquire Test Inferential statistics: t-tests Inferential statistics: correlation tests Inferential statistics:ANOVA and other tests Inferential statistics: Multivariate analysis Quiz and Questions