Assignment Task:
Let's say our research question was "Does priming people with words about being older make people walk slower when they leave the experiment?" Our best guess or research hypothesis (based on the research of Bargh, Chen, and Burrows, 1996) is that priming does make people walk slower. To conduct hypothesis testing we must do a few steps in this process. The first step is to create a null hypothesis. The null hypothesis, denoted by, is usually the hypothesis that sample observations result purely from chance. That is, the null hypothesis states that nothing is happening or different.
H0: There is no difference in walking speed whether people have been primed or not with words related to being older.
If we want to find significant results with our study, our goal is to knock down the null hypothesis. That is, we want our research to find differences so that we can say the null hypothesis is not true.
A hypothesis test can have one of two outcomes: you "reject" the null hypothesis or you "fail to reject" the null hypothesis. We do not accept the null hypothesis as acceptance implies that the null hypothesis is true. Failure to reject implies that the data are not sufficiently persuasive for us.
Hypothesis Tests
There is a formal process to determine whether to reject a null hypothesis, based on sample data. This process, called hypothesis testing, consists of four steps.
State the null hypotheses.
State a p-value. The p-value is the probability that the null hypothesis is true. If it is really small, then there is very little chance that the null hypothesis is true. That is why we often see p < .05 in research articles. This is stating that the probability of the null hypothesis is true is less that 5%.
Analyze the sample data. Find the value of the test statistic (mean score, proportion, t-score, z-score, etc.).
Interpret results. If the test statistic is significant (p-value is really small), then we can reject the null hypothesis. Need Assignment Help?
Decision Errors
Two types of errors can result from a hypothesis test.
Type I error. A Type I error occurs when the researcher rejects a null hypothesis when it is true. So it is a false positive (i.e., finding something that really does not exist). An example would be taking a pregnancy test that indicates a positive result even though the person is not pregnant.
Type II error. A Type II error occurs when the researcher fails to reject a null hypothesis that is false. A Type II error is a missed opportunity to find an effect that exists. The probability of not committing a Type II error is called the Power of the test.
Bargh, J. A., Chen, M., & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of personality and social psychology,71(2), 230.
With your knowledge of significance testing, read over Cohen (1990) and Schmidt (1996). Describe the significant flaws of null hypothesis testing and what it means to our field. (over the coming weeks, we will be discussing alternative approaches).
Cohen - 1990 - Things I have learned (so far).
Schmidt - 1996 - Statistical significance testing and cumulative kn