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Null hypothesis abbreviated as H0 (Is not typically stated; but it is the alternative to the research hypothesis) research hyp- abbreviated at H1. When you confirm the research hypothesis, you reject the null hypothesis Significance level or probability level: refers to the amount of error the researcher is willing to accept; tells you the likelihood that the result did not occur by chance or error. Statistical significance the amount of confidence you place on ensuring your findings truly represent those in the larger population One-tailed Test (Directional) testing the probability that the results will fall within a particular direction in the normal distribution. More stringent test because you are creating a larger region of rejection on one side of the normal curve. The researcher relies on past research to predict directionality. Two-tailed Test (non-directional) divides the region of rejection ino the two tails of the normal distribution. The region of rejection is smaller on each side. The researcher uses this when there is minimal information available based on the lit review. One-tail vs. Two-tail Two-tail specifies that there are differences in the relationship while the one-tail specifies the direction of those differences Type I Error (alpha) rejecting a null hypothsis that should be ACCEPTED as true. Under teh control fo the rsearcher because it is equal to the significance level set by the researcher. -To control, reseracher can set the level of significance used to test the hyp, at a lower level. -The level of Type I error is inversely proportional to the level of a Type II error (vice versa) Type II Error (beta) accpeting a null hypothesis that should be REJECTED. Controlled by the design of the research. Statistical Power Analysis How many research participants do I need for a study? Can help address that issue by offereing an estimate of the minimum number of particpatns needed to provide teh best chance of discovering if something does or does not exist Power (1 minus the probability of Type II error) the ability to detect differences that are truly different and is a function of the sample size; indicated teh probability that a stistical test of the null hypothesis will conclude that the phenomenon under study actually exists Statistical Power is a fuction of: 1. probability level (under the control of the researcher and the level predetermines the probability of the committing a Type I error. 2. sample size (number of research participants) 3. effects size (the degree to which teh null hypothesis is rejected; A larger effects size indicates the greater the degree to which the phenomenon under study is present.) Power is important because: 1. Low poer may prevent the researcher from finding statistical significance when sifinifance DOES exist: increasing power may enable the researcher to detect the stitistcal significance when it does exist power is important because: 2. high power may help interpret research results. If a statistical test barely reaches the established significance level, but has high power, researchers can be more confident in the results.