<?xml version="1.0" encoding="ISO-8859-1"?>
<questionset>

<dataset dsID="generated">
<distribution vname="Group 1">
<dname>normal</dname>
<sampleSize>8</sampleSize>
<parameter pname="decimal" pvalue="0"/>
<parameter pname="mean" pvalue="50.0" />
<parameter pname="sd" pvalue="10.0" />
</distribution>

<distribution vname="Group 2">
<dname>normal</dname>
<sampleSize>8</sampleSize>
<parameter pname="decimal" pvalue="0"/>
<parameter pname="mean" pvalue="40.0" />
<parameter pname="sd" pvalue="10.0" />
</distribution>


</dataset>


<dataset dsID="DATA0">
<variable vname="C1">
<obs> 5 6 2 3 5 5 7 6 8  5 4  7 3 5 6 </obs>
</variable>

<variable vname="C2">
<obs> 7 6 6 2 9 8 7 6 9 7 3 9 5 7 6 </obs>
</variable>


</dataset>

<dataset dsID="DATA1">
<distribution vname="C1">
<dname>normal</dname>
<sampleSize>8</sampleSize>
<parameter pname="decimal" pvalue="2"/>
<parameter pname="mean" pvalue="10"/>
<parameter pname="sd" pvalue="3"/>
</distribution>

<distribution vname="C2">
<dname>normal</dname>
<sampleSize>8</sampleSize>
<parameter pname="decimal" pvalue="2"/>
<parameter pname="mean" pvalue="12"/>
<parameter pname="sd" pvalue="3"/>
</distribution>


</dataset>

 
  <question type="numeric">
<q>  What would be Pearson's measure of skew for the following distribution: Mean = 50, Median = 60, and Variance = 100? </q>

<answer>
	<value>-3</value>
	<tolerance> 0.0</tolerance>
</answer>
<feedback>3(50-60)/sqrt(100) = -3 </feedback>
</question>

<question type="disNumeric">
<q>
What is the t for an independent-groups t-test for these data?
</q>

<datainfo dsID="generated">
<decimals>0</decimals>
<RandomNumVars>2</RandomNumVars>
</datainfo>

<answer stat="Ind_T_test">
	<tolerance> 0.01</tolerance>
</answer>

<feedback> Compute the t for an independent-groups t test. If you are using
 a program, make sure to recode the data as described. showCorrectAnswer
</feedback>

</question>



<question  type = "fixNumeric">
<q>Using these  data given, what is the  t statistic using a correlated pairs t-test</q>


<datainfo dsID="DATA0" >
<decimals>2 </decimals>
<RandomNumVars>2</RandomNumVars>
</datainfo>


<answer>
	<tolerance> 0.02</tolerance>
	<value>3.162</value>
</answer>

<feedback> Compute dependent t-test these two variables. In analysis lab, you can do a repeated-measures ANOVA and take the square root of F.  t = 3.162</feedback>
</question>


<question  type = "sc">
<q>Consider a within-subject experimental design with 2 conditions. The experimenter ran both a correlated pairs t-test and (incorrectly) an independent samples t-test. The experimenter found the correlated pair t-test was significant  whereas the independent samples test was not. The experimenter conlcuded that this was because the mean difference score across subjects was greater than the difference between the means of the conditions. Is this the correct conclusion?</q>

<feedback>The two mean differences are the same.  The correlated t test controlled for differences between subjects and thus reduced the standard error of the difference between the two means </feedback>

<choice>
<correct>false</correct>
<ctext>yes</ctext>

</choice>

<choice>
<correct>true</correct>
<ctext>no</ctext>

</choice>
</question>

<question  type = "sc">
<q> Correlated pairs t-tests generally have more power than independent sample t-tests because the former usually have smaller standard errors of the difference between means  making the t-value bigger. </q>

<feedback>By using each subject as its own control, the standard error is lower.</feedback>

<choice>
<correct>true</correct>
<ctext>True</ctext>

</choice>

<choice>
<correct>false</correct>
<ctext>False</ctext>

</choice>
</question>



<question  type = "sc">
	<q>Increasing the correlation between measures:</q>
	<feedback>All else being equal, the higher the correlation, the lower the standard error and therefore the higher the absolute value of t.</feedback>
	<choice>
		<correct>true</correct>
		<ctext>increases the absolute value of t.</ctext>
	</choice>

	
	
	
	
	<choice>
		<correct>false</correct>
		<ctext>decreases the absolute value of t.</ctext>
	</choice>
	
	<choice>
		<correct>false</correct>
		<ctext>has no effect on the t.</ctext>
	</choice>
</question>



<question  type = "sc">
<q>The null hypothesis in a correlated pairs t-test is that the population mean difference score is 0.</q>

<feedback>If the mean difference score is not 0, then the means of the two conditions will not be equal.</feedback>

<choice>
<correct>true</correct>
<ctext>True</ctext>

</choice>

<choice>
<correct>false</correct>
<ctext>False</ctext>

</choice>
</question>






</questionset>




