# Harnessing Statistical Insights: Jamovi Analysis, T-Tests, and Journal Findings

Dive into the world of statistical analysis as we unravel reaction time data, explore PE class performance shifts, and investigate the impact of dark chocolate on weight loss. From crafting jamovi spreadsheets to deciphering t-test outcomes, join us on a journey through diverse analytical challenges, culminating in an examination of a compelling journal article's statistical revelations.

## Problem 1: Reaction Time Analysis

### Problem Description:

The Jamovi assignment involves analyzing reaction time data from 20 participants using jamovi. The tasks include creating a jamovi spreadsheet, calculating z-scores, and answering specific questions based on the results.

Participant
ID#
Reaction Time (ms)
101 416
102 332
103 375
104 470
105 421
106 341
107 1262
108 359
109 396
110 392
111 431
112 464
113 496
114 438
115 493
116 1015
117 440
118 375
119 460
120 472

Table 1: Jamovi spreadsheet for participant ID and Reaction time

Solution: To analyze the data, a jamovi spreadsheet was created, and z-scores for reaction times were calculated. The following are the results:

a) Participant Closest to Mean:

• Answer: Participant 115 had a reaction time closest to the sample mean, with a z-score of 0.003.

b) Participants within ±0.5 Standard Deviations:

• Answer: 14 participants had reaction times within ±0.5 standard deviations of the sample mean.

c) Identifying Outliers:

• Answer: Participant 107, with a z-score of 3.351, would be removed as an outlier.

## Problem 2: PE Class Jumping Distance

### Problem Description:

Students' jumping distances at the beginning and end of the semester are analyzed. Questions involve hypothesis testing and result interpretation.

Student ID First Week Last Week
001 3 5
002 4 3
003 4 4
004 2 4
005 3 4
006 3 5
007 2 4
008 4 5
009 2 5
010 3 3

Table 2: A weekly testing for student’s jumping distance

Solution:

a) Rejecting Null Hypothesis:

• Answer: The null hypothesis of no change would be rejected as the difference in jumping distance is statistically significant at the 5% level.

b) Results Statement:

• Answer: A paired sample t-test showed significant improvement (t(9) = 3.1, p = .01, d = 0.2) in jumping distance over the semester.

c) Plain Explanation:

• Answer: On average, students jumped significantly farther in the last week compared to the first week, indicating improvement.

## Problem 3:Dark Chocolate and Diet

### Problem Description:

The effects of dark chocolate on a diet plan are investigated using an independent sample t-test.

Diet Diet + Chocolate
2 3
3 3
2 5
4 4
3 2
3 3
2 4
4 4
1 3
2 2

Table 3: The number of pounds each group lost at the end of the study.

Solution:

a) Rejecting Null Hypothesis:

• Answer: The null hypothesis is not rejected as the difference in weight loss is not significant (p = .1).

b) Results Statement:

• Answer: The t-test result (t = -1.6, p = .12, d = -0.7) indicates no significant effect of dark chocolate on weight loss.

c) Simple Explanation:

• Answer: Dark chocolate did not significantly impact weight loss; both groups showed similar results.

## Problem 4: Journal Article Analysis

Selected Article: a) [Brustkern, J., Heinrichs, M., Walker, M. et al. Facial threat affects trust more strongly than facial attractiveness in women than it does in men. Sci Rep 11, 22475 (2021).

b) Description:

• Answer: The study explores the sex-specific effects of facial attractiveness and threat on trust. The t-test (t(91) = 1.415, p = 0.161) found no significant differences.

c) Appropriateness of T-Test:

• Answer: An independent samples t-test was appropriate for comparing two independent groups (men and women).

d) Assumptions:

• Answer: The paper did not explicitly report the test of normal distribution.

These refined solutions provide a clear and structured presentation for your website, enhancing readability and comprehension.