Testing population proportion is a hypothesis testing procedure used to assess whether or not a sample from a population represents the true proportion of the entire population. Testing a sample population proportion is a widely used statistical method with various applications across different fields.
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Purpose of Testing Population Proportion (one-sample)
The main purpose of testing a sample population proportion is to make inferences about an entire population based on the sample information. Testing a sample population proportion helps to determine whether an observed sample proportion is significantly different from a hypothesized population proportion.
Common Uses of Testing Population Proportion
The following are some common uses of population proportion:
Marketing research: To determine if a certain proportion of customers prefer one product compared to another.
Quality control: In manufacturing, population proportion tests can be used to test/check if the proportion of defective items in a production batch exceeds an acceptable threshold.
Medical research: To test the efficacy of a new treatment by comparing the proportion of patients who recover using the new treatment versus a standard treatment.
Political polling: To estimate the proportion of voters supporting a particular candidate or policy.
Social sciences: To examine the prevalence of certain behaviors or attitudes in a population.
Applications Population Proportion in Various Fields
Business: Testing customer satisfaction rates, conversion rates in A/B testing for websites, or employee retention rates.
Public health: Estimating vaccination rates, disease prevalence, or the effectiveness of public health campaigns.
Education: Assessing the proportion of students meeting certain academic standards or the effectiveness of new teaching methods.
Psychology: Evaluating the proportion of individuals exhibiting certain behaviors or responses in experiments.
Environmental science: Measuring the proportion of samples that exceed pollution thresholds.
Types of Testing Population Proportion
There are two types of population proportion tests.
One-sample z-test for proportion:One-sample proportion tests are used when comparing a sample proportion to a known or hypothesized population proportion.
Two-sample z-test for proportions: Two-sample proportion tests are used when comparing proportions from two independent samples.
Assumptions and Considerations
The following are assumptions and considerations when testing population proportion:
The sample should be randomly selected and representative of the population.
The sample size (number of observations in the sample) should be large enough (typically $np$ and $n(1-p)$ should both be greater than 5, where $n$ is the sample size and $p$ is the proportion).
For two-sample tests, the samples should be independent of each other.
Interpretation: The results of these tests are typically interpreted using p-values or confidence intervals, allowing researchers to make statistical inferences about the population based on the sample data.
Data Frive Decisions from Proportion Tests
By using tests for population proportions, researchers and professionals can make data-driven decisions, validate hypotheses, and gain insights into population characteristics across a wide range of fields and applications.
Suppose, a random sample is drawn and the population proportion (say) $\hat{p}$ is measured and $n\hat{p}\ge 5$, $n\hat{q}\ge5$, the distribution of $\hat{p}$ is approximately normal with $\mu_{\hat{p}} =p$ and $\sigma_{\hat{p}}=\sqrt{\frac{pq}{n}}$. Also, suppose that one of the possible null hypotheses of the following form, when testing a claim about a population proportion is:
$H_o: p=p_o$ $H_o:p\ge p_o$ $H_o\le p_o$
For simplicity, we will assume the null hypothesis $H_o:p=p_o$. The standardized test statistics for a one-sample proportion test is
This random variable will have a standard normal distribution. Therefore, the standard normal distribution will be used to compute critical values, regions of rejection, and p-values, as we use it to test a mean using a large sample.
Example 1 (Defective Items): Testing Population Proportion
A computer chip manufacturer tests microprocessors coming off the production line. In one sample of 577 processors, 37 were found to be defective. The company wants to claim that the proportion of defective processors is only 4%. Can the company claim be rejected at the $\alpha = 0.01$ level of significance?
Solution:
The null and alternative hypotheses for testing the one-sample population proportion will be
$H_o:p=0.04$ $H_1:p\ne 0.04$
By focusing on the alternative hypothesis symbol ($\ne$), the test is two-tailed with $p_o=0.04$.
Looking up $Z=3.00$ in the standard normal table (area under the standard normal curve), we get a value of 0.9987. Therefore, $P(Z\ge 3.00) = 1-0.9987) = 0.0013$. Note that the test is two-tailed, the p-value will be twice this amount or $0.0026$.
Since the p-value ($0.0026$) is less than the level of significance ($0.01$), that is $0.0025 < 0.01$ (p-value < level of significance), we will reject the company’s claim. It means that the proportion of defective processors is not 4%, it is either less than 4% or more than 4%.
Example 2 (Opinion Poll): Testing Population Proportion
An opinion poll of 1010 randomly chosen/selected adults finds that only 47% approve of the president’s job performance. The president’s political advisors want to know if this is sufficient data to show that less than half of adults approve of the president’s job performance using a 5% level of significance.
Solution:
The null and alternative hypothesis of the problem above will be
$H_o:p\ge 0.50$ $H_1:p< 0.50$
By focusing on the alternative hypothesis symbol (<), the test is left-tailed with $p_o=0.50$.
The $\hat{p} = 0.47$. The standardized test statistics for one-sample population proportion will be
For a left-tailed test (for $\alpha = 0.05$), the $Z_o=-1.645$. Since $-1.91 < -1.645$, the null hypothesis should be rejected. So the data does support the claim that $p<0.50$ at the $\alpha=0.05$ level of significance.
The efficiency of an estimator is a measure of how well it estimates a population parameter compared to other estimators. It is possible to have more than one unbiased estimator of a parameter. We should have at least one additional criterion for choosing among the unbiased estimator of the parameter. Usually, unbiased estimators are compared in terms of their variances. Thus, the comparison of variances of estimators is described as a comparison of the efficiency of estimators.
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Use of Efficiency
The efficiency of an estimator is often used to evaluate an estimator through the following concepts:
Bias: An estimator is unbiased if its expected value equals the true parameter value ($E[\hat{\theta}]=\theta$). The efficiency of an estimator can be influenced by bias; thus, unbiased estimators are often preferred.
Mean Squared Error (MSE): Efficiency can also be measured using MSE, which combines both variance and bias. MSE is given by: MSE = $Var(\hat{\theta}) + Bias (\hat{\theta})^2$. An estimator with a lower MSE is more efficient.
Relative Efficiency: The relative efficiency compares the efficiency of two estimators, often expressed as the ratio of their variances: Relative Efficiency = $\frac{Var(\hat{\theta}_2)}{Var(\hat{\theta}_1)}, where $\hat{\theta}_1$ is the estimator being compared, and $\hat{\theta}_2$ is a competitor.
The efficiency of an estimator is stated in relative terms. If say two estimators $\hat{\theta}_1$ and $\hat{\theta}_2$ are unbiased estimators of the same population parameter $\theta$ and the variance of $\hat{\theta}_1$ is less than the variance of $\hat{\theta}_2$ (that is, $Var(\hat{\theta}_1) < Var(\hat{\theta}_2)$ then $\hat{\theta}_1$ is relatively more efficient than $\hat{\theta}_2$. The ration is $E=\frac{Var(\hat{\theta}_2)}{var(\hat{\theta}_1)}$ is a measure of relative efficiency of $\hat{\theta}_1$ with respect to the $\hat{\theta}_2$. If $E>1$, $\hat{\theta}_1$ is said to be more efficient than $\hat{\theta}_2$.
If $\hat{\theta}$ is an unbiased estimator of $\theta$ and $Var(\hat{\theta})$ is minimum compared to any other unbiased estimator for $\theta$, then $\hat{\theta}$ is said to be a minimum variance unbiased estimator for $\theta$.
It is preferable to make efficient comparisons based on the MSE instead of its variance.
where $E[\hat{\theta}-E(\hat{\theta})] = E(\hat{\theta}) – E(\hat{\theta})=0$
Question about the Efficiency of an Estimator
Question: Let $X_1, X_2, \cdots, X_n$ be a random sample of size 3 from a population with mean $\mu$ and variance \sigma^2$. Consider the following estimators of mean $\mu$:
Since $\frac{1}{3} < \frac{3}{8}$, that is, $Var(T_1) < Var(T_2). The $T_1$ is better estimator of $\mu$ than $T_2$.
Reasons to Use Efficiency of an Estimator
Optimal Use of Data: An efficient estimator makes the best possible use of the available data, providing more accurate estimates. This is particularly important in research, where the goal is often to make inferences or predictions based on sample data.
Reducing Uncertainty: Efficiency reduces the variance of the estimators, leading to more precise estimates. This is essential in fields like medicine, economics, and engineering, where precise measurements can significantly impact decision-making and outcomes.
Resource Allocation: In practical applications, using an efficient estimator can lead to savings in money, time, and resources. For example, if an estimator provides a more accurate estimate with less data, it can result in fewer resources needed for data collection.
Comparative Evaluation: Comparisons between different estimators help researchers and practitioners choose the best method for their specific context. Understanding efficiency allows one to select estimators that yield reliable results.
Statistical Power: Efficient estimators contribute to higher statistical power, which is the probability of correctly rejecting a false null hypothesis. This is particularly important in hypothesis testing and experimental design.
Robustness: While efficiency relates mostly to variance and bias, efficient estimators are often more robust to violations of assumptions (e.g., normality) in some contexts, leading to more reliable conclusions.
In summary, the efficiency of an estimator is vital as it directly influences the accuracy, reliability, and practical utility of statistical analyses, ultimately affecting the quality of decision-making based on those analyses.
The importance of dispersion in statistics cannot be ignored. The term dispersion (or spread, or variability) is used to express the variability in the data set. The measure of dispersion is very important in statistics as it gives an average measure of how much data points differ from the average or another measure. The measure of variability tells about the consistency in the data sets.
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The dispersion is a quantity that is far away from its center point (such as average). The data with minimum variation/variability with respect to its center point (average) is said to be more consistent. The lesser the variability in the data the more consistent the data.
Example of Measure of Dispersion
Suppose the score of three batsmen in three cricket matches:
Player
Match 1
Match 2
Match 3
Average Score
A
70
80
90
80
B
75
80
95
80
C
65
80
95
80
The question is which player is more consistent with his performance.
In the above data set the player whose deviation from average is minimum will be the most consistent player. So, the player B is more consistent than others. He shows less variation.
There are two types of measures of dispersion:
Absolute Measure of Dispersion
In absolute measure of dispersion, the measure is expressed in the original units in which the data is collected. For example, if data is collected in grams, the measure of dispersion will also be expressed in grams. The absolute measure of dispersion has the following types:
Range
Quartile Deviation
Average Deviation
Standard Deviation
Variance
Relative Measures of Dispersion
In the relative measures of dispersion, the measure is expressed in terms of coefficients, percentages, ratios, etc. It has the following types:
For the following grouped data, the range and coefficient of the range will be
Classes
Freq
Class Boundaries
65 – 84
9
64.5 – 84.5
85 – 104
10
84.5 – 104.5
105 – 124
17
104.5 – 124.5
125 – 144
10
124.5 – 144.5
145 – 164
5
144.5 – 164.5
165 – 184
4
164.5 – 184.5
185 – 204
5
184.5 – 204.5
Tota.
60
The upper class bound of the highest class will be $x_{min}$ and the lower class boundary of the lowest class will be $x_{min}$. Therefore, $x_{max}=204.5$ and $x_{min} = 64.5$. Therefore,
Average Deviation and Coefficient of Average Deviation
The average deviation is an absolute measure of dispersion. The mean/average of absolute deviation either taken from mean, median, or mode is called average deviation. Statistically, it is
From the above discussion and numerical examples, In statistics, the variability or dispersion is crucial. The following are some reasons for the importance of Dispersion in Statistics:
Understanding Data Spread: Variability gives insights into the spread or distribution of data, helping to understand how much individual data points differ from the average or some other measure.
Data Reliability: Lower variability in data can indicate higher reliability and consistency, which is key for making sound predictions and decisions.
Identifying Outliers: High variability can indicate the presence of outliers or anomalies in the data, which might require further investigation.
Comparing Datasets: Dispersion measures, such as variance and standard deviation, allow for the comparison of different datasets. Two datasets might have the same mean but different levels of dispersion, which can imply different data patterns or behaviors.
Risk Assessment: In fields like finance, assessing the variability of returns is crucial for understanding and managing risk. Higher variability often implies higher risk.
Statistical Inferences: Many statistical methods, such as hypothesis testing and confidence intervals, rely on the variability of data to make accurate inferences about populations from samples.
Balanced Decision Making: Understanding variability helps in making more informed decisions by providing a clearer picture of the data’s characteristics and potential fluctuations.
Overall, variability is essential for a comprehensive understanding of data, enabling analysts to draw meaningful conclusions and make informed decisions.