Question:

Considering both the probability value and effect size measure, what interpretations would you make about the findings? That is, what are your conclusions about the effects of leaving happy faces on checks?

Recall again that Rind & Bordia (1996) investigated whether or not drawing a happy face on customers' checks increased the amount of tips received by a waitress at an upscale restaurant on a universitycampus. Duringthelunchhourawaitressdrewahappy,smilingfaceonthechecksofa randomhalfofhercustomers. Theremaininghalfofthecustomersreceivedacheckwithnodrawing(18 points).

The tip percentages for the control group (no happy face) are as follows:

45% 39% 36% 34% 34% 33% 31% 31% 30% 30% 28% 28% 28% 27% 27% 25% 23% 22% 21% 21% 20% 18% 8%

The tip percentages for the experimental group (happy face) are as follows:

72% 65% 47% 44% 41% 40% 34% 33% 33% 30% 29% 28% 27% 27% 25% 24% 24% 23% 22% 21% 21% 17%

Group of answer choices

The p value that is calculated for the t-test is dependent on several factors including sample size and effect size. It is very likely that, given the effect size obtained, if the sample size was larger the study would have yielded statistically significant results. Therefore, it appears that the low statistical power (i.e., high Type II error) of the study resulting from the small sample size was probably mainly responsible for the lack of significant findings.

The p value that is calculated for the t-test is dependent on one factor: sample size. It is very likely that, given the large effect size obtained, if the sample size was larger the study would have yielded statistically significant results. Therefore, it appears that the strong statistical power of the study resulting from the small sample size was mainly responsible for the lack of significant findings.

The p value that is calculated for the t-test is dependent on several factors including sample size and effect size. It is very likely that, given the small effect size obtained, if the sample size was smaller the study would have yielded statistically significant results. Therefore, it appears that the low statistical power (i.e., high Type II error) of the study resulting from the large sample size was mainly responsible for the lack of significant findings.

The p value that is calculated for the t-test is dependent on several factors including sample size and effect size. It is very likely that, given the effect size obtained, if the sample size was smaller the study would have yielded statistically significant results. Therefore, it appears that the low statistical power (i.e., high Type II error) of the study resulting from the large sample size was mainly responsible for the lack of significant findings.

q Gauss' law may be written as '95: e O . Which of the following statements concerning the charge or is true? L!) The charge q is the sum of all charges outside the Gaussian surface. C) The electric eld due to q is zero inside the Gaussian surface. Q The charge q is the sum of all charges on the Gaussian surface. L) The charge q is the sum of all charges inside the Gaussian surface. L; The charge q is the sum of all charges inside and outside the Gaussian surface. Charged boop (with Barling R on an axis through the center a distance z away): Force AL--disc with Radius R on an axis through the renter a Newto Name: Electric field of a sphere [5 points] Charge is distributed throughout a sphere of radius R according to the formula: p(r) - Po (1 - R) R where r is the distance from the center of the sphere. This sphere is shown at the right. Two gaussian surfaces are drawn in the figure, one with radius r R. a. Determine the charge enclosed by the inner gaussian surface (with radius ri). b. Write an algebraic expression for the electric field at the surface of the inner gaussian surface (with radius 71). c. Determine the charge enclosed by the outer gaussian surface (with radius ry). d. Write an algebraic expression for the electric field at the surface of the outer gaussian surface (with radius ra). Continued on next page1. A bivariate normal random vector (X1, X2)' is defined by X1 = M1 + 01 2 (1) X2 = 12 + 02 2 1 - PU2) where U1 and U2 are independent and standard normal random variables, of and 02 are positive numbers, #1 and /2 are real numbers, and -1