Created by Abbey List
Welcome to Duke University's hypothesis testing app! Here, you can learn about the crucial aspects of conducting and interpreting a hypothesis test, including setting your hypotheses, calculating a test statistic and p-value, making your conclusions, and navigating types of possible errors in your test.
Often in research and data analysis, we want to investigate certain new claims that challenge what is currently believed, so we take some sample data and see whether or not it supports our new claim. However, as there can be significant variation in sample data, we may not know if the evidence our data provide is because it truly supports our new claim or if it was just the result of random fluctuation in the sample. Hypothesis testing is a reliable way to take sample data and test how strongly its evidence supports our new claim -- if the evidence is strong, we can reject the old beliefs and support the new; if it is weak, we cannot reject the old beliefs. To explore this procedure, check out our learning objectives, and then take a look at the images further down to see a sample pathway through our app! (Will add these)
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Identify appropriate null (
$H_0$ ) and alternative ($H_A$ ) hypotheses for a problem scenario, and effectively interpret test conclusions in terms of the hypotheses/practical scenario. -
Properly calculate and interpret test statistic, p-value, and alpha (
$\alpha$ ) level, and understand how to use them to make a conclusion. -
Correctly define and interpret Type I and Type II errors, and calculate their probabilities, given various parameters such as alpha (
$\alpha$ ), sample size, and testing power. Understand the conditions/assumptions under which each type of error may occur.