Two-sided test

Use a two-sided alternative when you want to detect any change rather than a change in a specific direction, which fits most research questions. Because both tails count, the p-value is twice the one-tail area whenever the null distribution is symmetric. For example, a test statistic of $z = 1.75$ gives a p-value of $2 \times 0.0401 = 0.0802$, against 0.0401 for the matching one-sided test. A two-sided test at $\alpha = 0.05$ (alpha, the significance level) usually agrees with the 95% confidence interval, though for proportions the two can disagree in close cases because they use different standard errors.