With the following data, how can we determine No Invisibility Cloak​​​N = Mean = _______acts α = ______ acts Invisibility cloak N = Mean = _______acts S = ______ acts

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With the following data, how can we determine No Invisibility Cloak​​​ N = Mean = _______acts α = ______ acts Invisibility cloak N = Mean = _______acts S = ______ acts
Invisibility Cloak: Independent samples t-test
This data set, "Invisibility Cloak", provides the number of mischievous acts committed by two groups of people, those with and those without
and invisibility cloak.
Variables:
Participant - Identification number of a participant.
Cloak - Experimental group (0 = withouth a cloak of invisibility. 1 = with a cloack of invisibility).
- Mischief - the number of mischievous acts committed by a participant.
%3D
%3D
This example JASP file demonstrates the use of an independent samples t-test. Specifically, we examine the adequacy of the null hypothesis
that the Invisibility Cloak has no effect on the number mischievous acts committed.
Reference:
Field, A. P. (2017). Discovering Statistics Using IBM SPSS Statistics (5th ed.). London: Sage. [Fictional data set]
The data set was constructed by Andy Field who therefore owns the copyright. Andy Field generously agreed that we can include the data
set in the JASP data library. This data set is also publicly available on the website that accompanies Andy Field's book,
https://edge.sagepub.com/field5e. Without Andy Field's explicit consent, this data set may not be distributed for commercial purposes, this
data set may not be edited, and this data set may not be presented without acknowledging its source (i.e., the terms of a CC BY-NC-ND
license).
Independent Samples T-Test
In order to run the analysis select Independent t-test under T-Tests. Drag your independent variable. e.g, Group membership, into the box
"Grouping Variable", and drag your dependent variable into the box "Dependent Variables" This will automatically start the analysis. See
further notes for a closer explanation.
Independent Samples T-Test
Test
Statistic
df
Mean Difference
SE Difference
Cohen's d
Mischief
Student
-1713
22.000
0.101
-1.250
0 730
0.101
-0.700
-0.700
Welch
-1.713
21.541
-1.250
0.730
Student
Welch
Participant
-8 152
-8.152
22 000
22.000
* 001
<001
-12 000
1.472
-3.328
-3.328
-12.000
1.472
This table shows the output for the independent samples t-test both the Student t-test and Welch version are shown. The differences
between the groups are not statistically significant at the 05 level, which means that the data do not provide much motivation to reject the
Transcribed Image Text:Invisibility Cloak: Independent samples t-test This data set, "Invisibility Cloak", provides the number of mischievous acts committed by two groups of people, those with and those without and invisibility cloak. Variables: Participant - Identification number of a participant. Cloak - Experimental group (0 = withouth a cloak of invisibility. 1 = with a cloack of invisibility). - Mischief - the number of mischievous acts committed by a participant. %3D %3D This example JASP file demonstrates the use of an independent samples t-test. Specifically, we examine the adequacy of the null hypothesis that the Invisibility Cloak has no effect on the number mischievous acts committed. Reference: Field, A. P. (2017). Discovering Statistics Using IBM SPSS Statistics (5th ed.). London: Sage. [Fictional data set] The data set was constructed by Andy Field who therefore owns the copyright. Andy Field generously agreed that we can include the data set in the JASP data library. This data set is also publicly available on the website that accompanies Andy Field's book, https://edge.sagepub.com/field5e. Without Andy Field's explicit consent, this data set may not be distributed for commercial purposes, this data set may not be edited, and this data set may not be presented without acknowledging its source (i.e., the terms of a CC BY-NC-ND license). Independent Samples T-Test In order to run the analysis select Independent t-test under T-Tests. Drag your independent variable. e.g, Group membership, into the box "Grouping Variable", and drag your dependent variable into the box "Dependent Variables" This will automatically start the analysis. See further notes for a closer explanation. Independent Samples T-Test Test Statistic df Mean Difference SE Difference Cohen's d Mischief Student -1713 22.000 0.101 -1.250 0 730 0.101 -0.700 -0.700 Welch -1.713 21.541 -1.250 0.730 Student Welch Participant -8 152 -8.152 22 000 22.000 * 001 <001 -12 000 1.472 -3.328 -3.328 -12.000 1.472 This table shows the output for the independent samples t-test both the Student t-test and Welch version are shown. The differences between the groups are not statistically significant at the 05 level, which means that the data do not provide much motivation to reject the
Factor
Assumption Checks
The assumption checks are not statistically significant at the .05 level.
Test of Normality (Shapiro-Wilk)
W
Mischief
0.913
0.231
0.973
0.936
Participant
0.967
0.876
0.967
0.876
Note. Significant results suggest a deviation from
normality.
Test of Equality of Variances (Levene's)
F
df
Mischief
0 468
0.545
7.593e-31
1
Participant
1.000
Descriptives
Click here to add text
Group Descriptives
Group
Mean
SD
SE
Mischief
12
3.750
5.000
1.913
0.552
1
12
1.651
0.477
Participant
12
6.500
3.606
1.041
12
18.500
3.606
1.041
Descriptives Plots
Click nere to add te
Transcribed Image Text:Factor Assumption Checks The assumption checks are not statistically significant at the .05 level. Test of Normality (Shapiro-Wilk) W Mischief 0.913 0.231 0.973 0.936 Participant 0.967 0.876 0.967 0.876 Note. Significant results suggest a deviation from normality. Test of Equality of Variances (Levene's) F df Mischief 0 468 0.545 7.593e-31 1 Participant 1.000 Descriptives Click here to add text Group Descriptives Group Mean SD SE Mischief 12 3.750 5.000 1.913 0.552 1 12 1.651 0.477 Participant 12 6.500 3.606 1.041 12 18.500 3.606 1.041 Descriptives Plots Click nere to add te
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