Two-tail tests
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If your alternative hypothesis has "≠", that means it's a two-tail test.
Scenario: The Delta Apple Pi chapter at Crammer Nation University claims that their brothers get on average 25 Tinder matches per day. You have a hunch that their daily Tinder matches does not actually equal that, so you collect a random sample of 35 Delta Apple Pi brothers' daily Tinder matches. You measure a mean of 23.5 daily matches with a standard deviation of 5.7 matches. Provide support for your claim using a hypothesis test with an alpha level of 0.05.
We'd be assessing the p-value (the area under the distribution curve) of a sample occurring with a sample parameter less than ("<") or greater than (">") the one we found.
PRO TIP: The reason we're looking on both sides is because we're claiming that true population parameter does not equal (≠) a certain value, it could potentially be greater than (>) or less than (<) said value!
The z-table only shows the p-values for one tail of the z-distribution...
...as well as the t-table for the t-distribution...
...therefore...
Whenever working with a two-tail hypothesis test, make sure to double your p-value, since it will be reflected on both tails of the sampling distribution!