Interpreting results from simplified analysis involves understanding the context and limitations of the data. For example:
Descriptive statistics provide a snapshot of the data but do not imply causation. Proportions can indicate the extent of a health issue but must be considered alongside sample size and population characteristics. Cross-tabulation results can suggest associations but require further analysis to establish causality. Chi-square test results can indicate whether an association is statistically significant, but the strength of the association should be evaluated with additional measures. T-test results can show differences between groups but should be interpreted with caution regarding sample size and variance homogeneity. Correlation coefficients indicate the degree of linear relationship but do not imply causation.
