AP Statistics Curriculum 2007 Hypothesis Proportion
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==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Testing a Claim about Proportion== | ==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Testing a Claim about Proportion== | ||
- | === | + | === Background=== |
- | + | [[AP_Statistics_Curriculum_2007_Estim_Proportion |Recall that for large samples]], the sampling distribution of the sample proportion <math>\hat{p}</math> is approximately Normal, by [[AP_Statistics_Curriculum_2007_Limits_CLT |CLT]], as the sample proportion may be presented as a [[AP_Statistics_Curriculum_2007_Limits_Norm2Bin |sample average or Bernoulli random variables]]. When the sample size is small, the normal approximation may be inadequate. To accommodate this, we will modify the '''sample-proportion''' <math>\hat{p}</math> slightly and obtain the '''corrected-sample-proportion''' <math>\tilde{p}</math>: | |
- | Recall that for large samples, the sampling distribution of the sample proportion <math>\hat{p}</math> is approximately Normal, by [[AP_Statistics_Curriculum_2007_Limits_CLT |CLT]], as the sample proportion may be presented as a [[AP_Statistics_Curriculum_2007_Limits_Norm2Bin |sample average or Bernoulli random variables]]. When the sample size is small, the normal approximation may be inadequate. To accommodate this, we will modify the '''sample-proportion''' <math>\hat{p}</math> slightly and obtain the '''corrected-sample-proportion''' <math>\tilde{p}</math>: | + | : <math>\hat{p}={y\over n} \longrightarrow \tilde{p}={y+0.5z_{\alpha \over 2}^2 \over n+z_{\alpha \over 2}^2},</math> |
- | : <math>\hat{p}={y\over n} \longrightarrow \tilde{ | + | |
where [[AP_Statistics_Curriculum_2007_Normal_Critical | <math>z_{\alpha \over 2}</math> is the normal critical value we saw earlier]]. | where [[AP_Statistics_Curriculum_2007_Normal_Critical | <math>z_{\alpha \over 2}</math> is the normal critical value we saw earlier]]. | ||
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: <math>SE_{\hat{p}} = \sqrt{\hat{p}(1-\hat{p})\over n} \longrightarrow SE_{\tilde{p}} = \sqrt{\tilde{p}(1-\tilde{p})\over n+z_{\alpha \over 2}^2}.</math> | : <math>SE_{\hat{p}} = \sqrt{\hat{p}(1-\hat{p})\over n} \longrightarrow SE_{\tilde{p}} = \sqrt{\tilde{p}(1-\tilde{p})\over n+z_{\alpha \over 2}^2}.</math> | ||
- | === Hypothesis Testing about a | + | === Hypothesis Testing about a Single Sample Proportion=== |
- | * Null Hypothesis: <math>H_o: p=p_o</math> (e.g., | + | * Null Hypothesis: <math>H_o: p=p_o</math> (e.g., <math>p_o={1\over 2}</math>), where ''p'' is the population proportion of interest. |
* Alternative Research Hypotheses: | * Alternative Research Hypotheses: | ||
- | ** One sided (uni-directional): <math>H_1: p >p_o</math>, or <math> | + | ** One sided (uni-directional): <math>H_1: p >p_o</math>, or <math>H_1: p<p_o</math> |
** Double sided: <math>H_1: p \not= p_o</math> | ** Double sided: <math>H_1: p \not= p_o</math> | ||
- | * Test Statistics: <math>Z_o={\tilde{p} -p_o \over SE_{\tilde{p}}</math> | + | * Test Statistics: <math>Z_o={\tilde{p} -p_o \over SE_{\tilde{p}}} \sim N(0,1).</math> |
===Example=== | ===Example=== | ||
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And the corresponding test statistics is | And the corresponding test statistics is | ||
- | : <math>Z_o={\tilde{p} - 0.04 \over SE_{\tilde{p}}={0.002 \over 0.0085}=0.2353</math> | + | : <math>Z_o={\tilde{p} - 0.04 \over SE_{\tilde{p}}}={0.002 \over 0.0085}=0.2353</math> |
The p-value corresponding to this test-statistics is clearly insignificant. | The p-value corresponding to this test-statistics is clearly insignificant. | ||
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* Test Statistics: <math>Z_o = {Estimate-HypothesizedValue\over SE(Estimate)} = {\hat{p}_1 - \hat{p}_2 - 0 \over SE(\hat{p}_1 - \hat{p}_2)} = {\hat{p}_1 - \hat{p}_2 - 0 \over \sqrt{{\hat{p}_1(1-\hat{p}_1)\over n_1} + {\hat{p}_2(1-\hat{p}_2)\over n_2}}} \sim N(0,1)</math> and <math>Z_o=5.4996</math>. | * Test Statistics: <math>Z_o = {Estimate-HypothesizedValue\over SE(Estimate)} = {\hat{p}_1 - \hat{p}_2 - 0 \over SE(\hat{p}_1 - \hat{p}_2)} = {\hat{p}_1 - \hat{p}_2 - 0 \over \sqrt{{\hat{p}_1(1-\hat{p}_1)\over n_1} + {\hat{p}_2(1-\hat{p}_2)\over n_2}}} \sim N(0,1)</math> and <math>Z_o=5.4996</math>. | ||
- | * <math> | + | * <math>P_{value} = P(Z>Z_o)< 1.9\times 10^{-8}</math>. This small p-values provides extremely strong evidence to reject the null hypothesis that there are no differences between the proportions of mothers that had a girl as a second child but had either boy or girl as their first child. Hence there is strong statistical evidence implying that genders of siblings are not independent. |
* '''Practical significance''': The practical significance of the effect (of the gender of the first child on the gender of the second child, in this case) can only be assessed using [[AP_Statistics_Curriculum_2007#Estimating_a_Population_Proportion |confidence intervals]]. A 95% <math>CI (p_1- p_2) =[0.033; 0.070]</math> is computed by <math>p_1-p_2 \pm 1.96 SE(p_1 - p_2)</math>. Clearly, this is a practically negligible effect and no reasonable person would make important prospective family decisions based on the gender of their (first) child. | * '''Practical significance''': The practical significance of the effect (of the gender of the first child on the gender of the second child, in this case) can only be assessed using [[AP_Statistics_Curriculum_2007#Estimating_a_Population_Proportion |confidence intervals]]. A 95% <math>CI (p_1- p_2) =[0.033; 0.070]</math> is computed by <math>p_1-p_2 \pm 1.96 SE(p_1 - p_2)</math>. Clearly, this is a practically negligible effect and no reasonable person would make important prospective family decisions based on the gender of their (first) child. | ||
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<hr> | <hr> | ||
- | === | + | ===[[EBook_Problems_Hypothesis_Proportion|Problems]]=== |
+ | |||
+ | ===Notes=== | ||
+ | * [http://sciencenow.sciencemag.org/cgi/content/full/2009/1030/1 Read this Science discussion "Mission Improbable: A Concise and Precise Definition of P-Value"]. | ||
+ | * Use these data to investigate if there are significant gender effects on mortality rates in [[SOCR_Data_AMI_NY_1993_HeartAttacks|this study of heart attacks/ Acute Myocardial Infarction]]. | ||
<hr> | <hr> |
Current revision as of 00:16, 24 April 2013
Contents |
General Advance-Placement (AP) Statistics Curriculum - Testing a Claim about Proportion
Background
Recall that for large samples, the sampling distribution of the sample proportion is approximately Normal, by CLT, as the sample proportion may be presented as a sample average or Bernoulli random variables. When the sample size is small, the normal approximation may be inadequate. To accommodate this, we will modify the sample-proportion slightly and obtain the corrected-sample-proportion :
where is the normal critical value we saw earlier.
The standard error of also needs a slight modification
Hypothesis Testing about a Single Sample Proportion
- Null Hypothesis: H_{o}:p = p_{o} (e.g., ), where p is the population proportion of interest.
- Alternative Research Hypotheses:
- One sided (uni-directional): H_{1}:p > p_{o}, or H_{1}:p < p_{o}
- Double sided:
- Test Statistics:
Example
Suppose a researcher is interested in studying the effect of aspirin in reducing heart attacks. He randomly recruits 500 subjects with evidence of early heart disease and has them take one aspirin daily for two years. At the end of the two years, he finds that during the study only 17 subjects had a heart attack. Use α = 0.05 to formulate a test a research hypothesis that the proportion of subject on aspirin treatment that have heart attacks within 2 years of treatment is p_{o} = 0.04.
And the corresponding test statistics is
The p-value corresponding to this test-statistics is clearly insignificant.
Genders of Siblings Example
Is the gender of a second child influenced by the gender of the first child, in families with >1 kid? Research hypothesis needs to be formulated first before collecting/looking/interpreting the data that will be used to address it. Mothers whose 1^{st} child is a girl are more likely to have a girl, as a second child, compared to mothers with boys as 1^{st} children. Data: 20 yrs of birth records of 1 Hospital in Auckland, New Zealand.
Second Child | ||||
Male | Female | Total | ||
First Child | Male | 3,202 | 2,776 | 5,978 |
Female | 2,620 | 2,792 | 5,412 | |
Total | 5,822 | 5,568 | 11,390 |
Let p_{1}=true proportion of girls in mothers with girl as first child, p_{2}=true proportion of girls in mothers with boy as first child. The parameter of interest is p_{1} − p_{2}.
- Hypotheses: H_{o}:p_{1} − p_{2} = 0 (skeptical reaction). H_{1}:p_{1} − p_{2} > 0 (research hypothesis).
Second Child | ||||
Number of births | Number of girls | Proportion | ||
Group | 1 (Previous child was girl) | n_{1} = 5412 | 2792 | |
2 (Previous child was boy) | n_{2} = 5978 | 2776 |
- Test Statistics: and Z_{o} = 5.4996.
- . This small p-values provides extremely strong evidence to reject the null hypothesis that there are no differences between the proportions of mothers that had a girl as a second child but had either boy or girl as their first child. Hence there is strong statistical evidence implying that genders of siblings are not independent.
- Practical significance: The practical significance of the effect (of the gender of the first child on the gender of the second child, in this case) can only be assessed using confidence intervals. A 95% CI(p_{1} − p_{2}) = [0.033;0.070] is computed by . Clearly, this is a practically negligible effect and no reasonable person would make important prospective family decisions based on the gender of their (first) child.
- This SOCR Analysis Activity illustrates how to use the SOCR Analyses to compute the p-values and answer the hypothesis testing challenge.
Problems
Notes
- Read this Science discussion "Mission Improbable: A Concise and Precise Definition of P-Value".
- Use these data to investigate if there are significant gender effects on mortality rates in this study of heart attacks/ Acute Myocardial Infarction.
- SOCR Home page: http://www.socr.ucla.edu
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