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A Type I Error is when the null hypothesis (H0) is actually true, but we reject it.
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 are actually lower than 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.
The alternative states that Delta Apple Pi actually gets less than 25 average Tinder matches per day.
So if we reject the null when we shouldn't have...
Type I Error ➡️ We wrongly suggest that Delta Apple Pi's Tinder game is lower than it truthfully is! They actually have game!
The probability of committing a Type I Error is your alpha level.
That's the "p-value threshold" of your sample that you're comfortable accepting to reject the null... and in rejecting, you're accepting an alpha level probability that you wrongly reject it (a.k.a. Type I Error).
