What do Type I error and Type II error mean in hypothesis testing?

A researcher tests $H_0:p=0.45$ against an appropriate alternative at $\alpha =0.05$. The test leads to rejection of $H_0$, but in reality the true population proportion is $p=0.45$. Classify this decision error.

You draw $150$ simple random samples of size $n=30$ from a population with true proportion $p=0.2$. You perform a one-sided test $H_0:p=0.2$ versus $H_a:p>0.2$ at $\alpha =0.01$. Which description correctly identifies a Type I error in this simulation, and what is the approximate number of such errors expected among the $150$ samples?

A researcher decides to change the significance level of their hypothesis test from $\alpha =0.05$ to $\alpha =0.01$. Assuming the sample size and true effect size remain the same, which of the following is the most likely consequence?

A government agency must set a significance level for testing a safety device, as a false approval could endanger lives. Given the options $\alpha =0.10$, $\alpha =0.05$, and $\alpha =0.001$, which should they pick and why?