With the following data, how can we determine No Invisibility CloakN = Mean = _______acts α = ______ acts Invisibility cloak N = Mean = _______acts S = ______ acts Invisibility Cloak: Independent samples t-testThis data set, "Invisibility Cloak", provides the number of mischievous acts committed by two groups of people, those with and those withoutand 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%3DThis example JASP file demonstrates the use of an independent samples t-test. Specifically, we examine the adequacy of the null hypothesisthat 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 dataset 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, thisdata 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-NDlicense).Independent Samples T-TestIn 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. Seefurther notes for a closer explanation.Independent Samples T-TestTestStatisticdfMean DifferenceSE DifferenceCohen's dMischiefStudent-171322.0000.101-1.2500 7300.101-0.700-0.700Welch-1.71321.541-1.2500.730StudentWelchParticipant-8 152-8.15222 00022.000* 001<001-12 0001.472-3.328-3.328-12.0001.472This table shows the output for the independent samples t-test both the Student t-test and Welch version are shown. The differencesbetween the groups are not statistically significant at the 05 level, which means that the data do not provide much motivation to reject the FactorAssumption ChecksThe assumption checks are not statistically significant at the .05 level.Test of Normality (Shapiro-Wilk)WMischief0.9130.2310.9730.936Participant0.9670.8760.9670.876Note. Significant results suggest a deviation fromnormality.Test of Equality of Variances (Levene's)FdfMischief0 4680.5457.593e-311Participant1.000DescriptivesClick here to add textGroup DescriptivesGroupMeanSDSEMischief123.7505.0001.9130.5521121.6510.477Participant126.5003.6061.0411218.5003.6061.041Descriptives PlotsClick nere to add te

Independent samples t-test is a useful inferential procedure in statistics.

Answered: With the following data, how can we…

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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

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

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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