A hypothesis is a statement of expected relationship between two or more variables. - Theoretical and empirical justifications. - Testable. - Brief wording For example: There is a gender difference in the perception of body sensations. Women and men use physiological cues (internal) and situational factors (external) differently in defining bodily state. Women, compared to men, make greater use of external cues in defining their body sensations. There is a relationship between information processing techniques and subsequent recall of information. Visual imagery has a greater enhancing effect on recall than verbal recitation. People tend to apply dispositional attribution to account for behaviours of others and use situational attribution to explain behaviours of themselves. Induced self-consciousness enhances recall of personal information. Teachers who use specific feedback during lectures obtain higher pupil achievement gains than teachers who use general feedback. High intimacy self-disclosing statements would be more effective in counselling than low intimacy self-disclosing statements.

Hypothesis Testing 1.Research hypothesis reflecting your verbal reasoning. The wording often reflects the research design. - There is a relationship between motivation to learn and math achievement. - Girls have higher math achievement than boys. - The effect of induced public self-consciousness is stronger on adolescents than adults. 2. Statistical hypothesis reflecting the statistics used to summarize your observations. ρ > 0: There is a positive correlation between motivation to learn and math achievement. The statistic of correlation is used to summarize data. μg > μb: Mean math achievement of girls is higher than that of boys. Mean is used to summarize data.

3. Null hypothesis representing a way to test the statistical hypothesis. μg = μb. The mean math achievement of girls is the same as the mean of boys. ρ = 0. There is no correlation between motivation to learn and math achievement. 4. Statistical tests are conducted with the assumption that the null hypothesis is true. - What is the probability of finding a positive correlation when the truth is that there is no correlation? - What is the probability of finding a difference between the two means when there is no difference?

Statistical Significance - The probability level at which you will reject the null hypothesis, or, at which you will allow yourself the risk of wrongly rejecting the null hypothesis. Type I Error - Significance level is also Type I error rate. It is the probability of rejecting the null hypothesis when the null hypothesis is true. You make such an error only when the null is rejected. Type II Error - It is the probability of not rejecting the null hypothesis when the null hypothesis is false. You make such an error only when you fail to reject the null hypothesis.

μ E –μ C = 0 z =2.33,  <.01 Directional Hypothesis One Tailed Test

μ 1 –μ 2 = 0  < 0.01/2 <.005 z critical = 2.58 Non-directional Hypothesis Two Tailed Test

Four steps in hypothesis testing 1.State the null and alternative hypotheses. 2.Set the level of statistical significance which is the probability at which you will reject the null or at which you will allow yourself to make the type I error. Example H0: μ1-μ2 = 0 H1: μ1-μ2 > 0 α<.05 t (.05, 28)=1.7

3. Compute the test statistic which can be a t-test, z- test, f-test, chi-square, etc. 4. Decisions about the null. If you reject the null, you may make a type I error probability of which is set at step 2. If you do not reject the null, you are sunning the risk of making a type II error the probability of which can be estimated. t (28) = 2.85 Reject null and support your research (alternative) hypothesis

Hypothesis: - People high in public self-consciousness are more conforming to perceived social norms about gender roles (than those low in public self-consciousness). - There is a relationship between public self-consciousness and gender role conformity. - Independent variable is public self-consciousness. - Dependent variable is gender role conformity. Operational definitions: - Public self-consciousness is measured by the Self-Consciousness Scale (Fenigstein, Scheier, & Buss, 1975; Scheier & Carver, 1985). - Gender role conformity is defined by the following operations: Ten gender role attitudes questions were used to first determine participants' own standings on these gender role questions. The participants were then informed of the mean ratings of their peers on these gender role questions and were asked to re-assess their attitudes toward these gender roles. Conformity to social norms on gender roles is measured by the difference score between the two self-assessment on the ten gender roles questions.

Statistical hypothesis: μhigh public – μ low public > 0 This implies that the statistic, mean, is used to summarize sampled data that bear on the hypothesis. OR ρ > 0, implying that the statistic, correlation, is used to summarize data. Null hypothesis: μhigh public – μ low public = 0 OR ρ = 0 Significance level: α <.05

Hypothesis testing rationale The hypothesis is regarding population. The null assumes that there is no relation or no difference. The research hypothesis assumes that there is a relation. The purpose of hypothesis testing is to make the qualitative decision regarding whether my sample is taken from the populations defined by the null (decision: accept null and your research hypothesis is not supported) or is taken from the population defined by the alternative (research) hypothesis (decision: reject null and your research hypothesis is supported). Hypothesis testing starts with the assumption that the null is true. Even though the null is true, there is a chance that your sample statistic differs from the population parameter. What is the chance? Significance level But if the chance is so small or smaller than the significance level, you are willing to run the risk of rejecting the null and make the decision that your sample is not taken from the population defined by the null. That decision is associated with Type I error, which however is not bigger than the significance level. Because you reject the null at the significance level, you call your results (finding, study) “statistically significant” or simply “significant.”

 : Type II error  : Type I error Power Actually TrueActually False NOT reject Reject Decision Null Hypothesis

μ = 50 z = 1.96 H 0 : μ = 50 H 1 : μ > Reject Null β power

μ = 50 z = 1.96 H 0 : μ = 50 H 1 : μ > 50 Reject Null β power.01

power β μ = 50 H 0 : μ = 50 H 1 : μ > Reject Null z = 1.96 Large N

Small N power β.05 H 0 : μ = 50 H 1 : μ > 50 Reject Null μ = 50z = 1.96