Are one-sided tests inferior to two-sided tests? I recently did a one-sided test for a homework question. This ended up being graded as correct. However, one of my group mates is a stat major and he said that most professional statisticians use only two-sided tests because using a one-sided test halves the p-value and therefore makes reporting results less accurate or misleading. 
Are one-sided tests somehow inferior to two-sided tests? Is it improper to use one-sided tests in practice? 
I don't understand what the point is in teaching them if we aren't supposed to use them for actual statistical work. 
 A: Your group mate doesn't know what he's talking about.  It depends on what you're interested in. For example, if you were a pharmaceutical company trying to determine whether a new drug lowers blood pressure, you'd want $H_0 : \theta \geq 0$ and $H_A : \theta < 0$ where $\theta$ is, say, the difference in before and after blood pressure.
Regarding "halving the p-value" what that refers to is that for your garden-variety t-test for a mean, at the same value of $\bar{x}$, your sample mean, the p-value is doubled because under the null hypothesis you are computing $P(|\frac{\bar{x}}{s/\sqrt{n}}| > Z)$ or $P(-|\frac{\bar{x}}{s/\sqrt{n}}| < Z)$ where $Z$ is a standard normal random variable. This is because we only care about the magnitude of the difference.
On the other hand, for a one-sided test you only care about one direction. So we could compute just $P(\frac{\bar{x}}{s/\sqrt{n}} > Z)$ for example. This is related to the idea of statistical power. Yes, the p-value is lower for the same value of the sample mean, but this could be good or bad. If you're only going to be going forward with a drug trial if it reduces the symptoms, it could make things worse for all you care and that could be just as much a reason not to continue as if it does nothing.
A: 
However, one of my group mates is a stat major and he said that most professional statisticians use only two-sided tests because using a one-sided test halves the p-value and therefore makes reporting results less accurate or misleading.

This is silly. I would say that's an inaccurate statement.
Even if true, there are far worse things that could increase you False Discovery Rate (FDR) such as data that does not follow a nice Normal distribution, or for nonparametric tests, data that does not follow assumptions. Another item often ignored is large sample sizes, which can also affect FDR.
One sided tests are valid if the research question is concerned with a single side of the distribution.
