Hypothesis Tests The t-test below was conducted on measurements from a high performance ceramics experiment with the goal of characterizing the effect of grinding parameters on sintered reaction-bonded silicon nitride. The experimenters were interested, in, among other things, if ceramic strength [in MPa] differed across batches A and B, using 16 samples from each batch. Use alpha = 0.1. t.test(Strength Batch, silveNitrade, conf.level=0.9, alternative = "t") Welch Two Sample t-test data: Y by atch t = 1.6813, df = 29.585, p-value = 0.1032 alternative hypothesis: true difference in means between group A and group B is not equal to 0 90 percent confidence interval: -0.643492 130.362242 sample estimates: mean in group A mean in/group B 579.3256 514.4662 cohen.d(Strength Batch, silveNitrade, conf.level = 0.9) Cohen's d d estimate: 0.5944431 (medium) 90 percent confidence interval: lower upper -0.01873877 1.20762491

Charlie will perform a hypothesis test at the a = 0.05 level. Felice will perform the same test at the a = 0.01 level.

A null and alternative hypothesis are given. Determine whether the hypothesis test is left-tailed, right-tailed, or two-tailed. Họ: o 2 2.8 Ha: o < 2.8 What type of test is being conducted in this problem? O A. Two-tailed test B. Left-tailed test OC. Right-tailed test

Can you state the null hypothesis H0 and the alternative hypothesis H1? .