Toward that end, you will compose in each case a Null Hypothesis (denoted \(N_0\)). This statement is what you will test for truth. You will also compose an Alternate Hypothesis (denoted \(N_a\)) that is often the logical complement (but not always) of \(N_0\text{.}\) The null hypothesis is often a statement corresponding to the likelihood that observations occur purely by chance while the alternate hypothesis will often indicate that outcomes are not actually random but are influenced by some (possibly unknown) causes. If our sample shows that the null hypothesis \(N_0\) is false, then we will accept the alternate \(N_a\text{.}\) If this is a bad decision then it will be true that the hypothesis is true but you will have determined that it is false...that is, made a Type I error.