Hypothesis Testing: Comparing the Null & Alternative Hypothesis

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  • 0:05 Null & Alt
  • 1:16 Significance
  • 2:39 Phrasing
  • 4:10 Lesson Summary
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Lesson Transcript
Instructor: Devin Kowalczyk

Devin has taught psychology and has a master's degree in clinical forensic psychology. He is working on his PhD.

This lesson explores the process of comparing the null and the alternative hypothesis, as well as how to differentiate between the two after your testing is done.

Null & Alt

Two scientists walk into a bar; the first is named Null, and the second is named Alt. The two walk up to the bar itself, and the bartender asks, 'What'll it be?'

Null says, 'I bet that if you drink the beer here that nothing will happen!'

To which Alt replies, 'I bet the beer here will get you drunk!'

The two scientists drink long into the night until Null blacks out and, because he passed out, was rejected by the bar patrons and thrown out into the dumpster.

This is a bizarre but easy to visualize example of how one remembers what a hypothesis is and how they are tested. A null hypothesis is typically the standard assumption and is defined as the prediction that there is no interaction between variables. The symbol for the null hypothesis is 'Hsub0'. This is opposed by the alternative hypothesis, also known as the research hypothesis, defined as the prediction that there is a measurable interaction between variables. The symbol for the alternative hypothesis is 'Hsub1'.


When we are testing our hypothesis, we will run a statistical procedure to determine if there is a significant relationship. Statistically significant means the difference in the results did not occur by random chance. This is almost always represented by a lower case 'p'. Another term you may also hear for this is alpha, and it may be represented by the alpha symbol (the one that looks like a little fish).

What a p-value tells you is the percentage that the results are caused by random chance. If you have a p-value of .75, that means the numbers were so close that any difference is likely to occur 3 times out of 4. In psychology, an experiment seen as statistically significant has a p-value of less than .05.

Often, when writing up the results of an experiment you will use the phrase 'statistically significant' or 'not statistically significant'. This lets you know whether or not the experiment demonstrated a difference, relationship, or interaction. This is at the heart of hypothesis testing. There are dozens of statistical tests to run, and nearly all of them have a statistically significant component to let you know if the differences are viable.

But you aren't done here. When your numbers demonstrate an interaction, this means you need to report it in the correct way.


After testing a hypothesis, like 'will you drunk,' you will assume that the null hypothesis is true, but the testing is to see if your alternative hypothesis is accurate. Now this is where things get a little complicated because the language of an experiment shifts from our everyday vernacular.

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