What Type of Error Was That Type I or Type II Error - Essay Example

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The independent variable was happiness, and the dependent variable was positive affect. The sampling that I would use is the binomial…

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What Type of Error Was That Type I or Type II Error

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Subject: Psychology
Type: Essay
Level: Ph.D.
Pages: 2 (500 words)
Downloads: 2
Author: bemard

Extract of sample "What Type of Error Was That Type I or Type II Error"

What Type of Error Was That? Type I or Type II? The hypothesis that I wrote for the last discussion is: People who are naturally happy are more successful in life than those who are not happy. The independent variable was happiness, and the dependent variable was positive affect. The sampling that I would use is the binomial distribution because there would be two different variable. I would have those people who had a more propensity for being happy and those who had more of a propensity for being sad.

According to the textbook, the binomial distribution is for events that have two different possible answers. My project would have as one answer, “Do you see yourself as always happy?” and the other question would be, “Do you see yourself as always unhappy?” the individuals would have an equal probability of saying they were happy or unhappy. The reason I would use this sampling is because it seems as though it would be the easiest to understand in order to do this type of problem.Type I or Type II Error If I were analyzing my statistics, I would make a Type I error if I thought that the people who said they were happier than others was really unhappy.

As an example, since I would be testing for those people who were happy and successful versus those who were unhappy and unsuccessful, I would have a Type 1 Error if I thought some of the people who actually were unhappy and unsuccessful were successful. This would create a problem within the sample. In this process, it would also mean that I had rejected the null hypothesis when it was actually true. If I made a Type II Error, this would mean that I failed to reject the null hypothesis when it was actually false.

As an example, if I saw some of the people who said they were happy and successful as people who were unhappy and unsuccessful, I would have a false null hypothesis. “Statistically significant” refers to the relationship between the p-value and the alpha. It is a standard that means that the null hypothesis can be rejected only in a situation where the data that we are observing is “so unusual” that they would have occurred “by chance” only five percent of the time (p. 147). When the alpha is smaller, the standard becomes more stringent.

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