One-sided and two-sided hypothesis tests differ fundamentally in their alternative hypotheses: one-sided tests restrict inference to a single tail of the sampling distribution (directional inequality), while two-sided tests evaluate deviations in either direction from the null value. The theoretical distinction lies not merely in halving the P-value, but in defining distinct rejection regions that alter Type I error rates; switching test directions post-data observation effectively doubles the significance level and constitutes a two-tailed test by design rather than valid inference strategy. This framework belongs to statistical hypothesis testing within inferential statistics, governing how evidence is weighed against null hypotheses under specified directional constraints or symmetry assumptions regarding population parameters.
One-sided and two-sided hypothesis tests differ fundamentally in their alternative hypotheses: one-sided tests restrict inference to a single tail of the sampling distribution (directional inequality…