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Related Concept Videos

Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Surveys02:16

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Related Experiment Video

Updated: Apr 24, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Estimating population treatment effects from a survey subsample.

Kara E Rudolph, Iván Díaz, Michael Rosenblum

    American Journal of Epidemiology
    |September 6, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Estimating treatment effects in survey subsamples is challenging. Doubly robust estimators offer a superior, easy-to-implement alternative to inverse probability weighting for generalizing findings to target populations.

    Keywords:
    causal inferenceinverse probability weightingsurveytargeted maximum likelihood estimation

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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Survey Methodology

    Background:

    • Estimating average treatment effects (ATE) from survey data requires accounting for complex sampling designs and nonrandom selection.
    • Generalizing findings from subsamples to target populations is crucial in epidemiological research.
    • The National Comorbidity Survey Replication Adolescent Supplement provides a relevant dataset for studying adolescent mental health treatment effects.

    Purpose of the Study:

    • To evaluate and compare statistical methods for estimating ATE in a target population using survey subsamples.
    • To assess the performance of inverse probability weighting (IPW) versus doubly robust (DR) estimators.
    • To provide practical guidance for applied epidemiologic researchers on selecting appropriate estimation methods.

    Main Methods:

    • Comparison of Horvitz-Thompson estimator with IPW and two DR estimators.
    • Evaluation across various scenarios, including potential model misspecification.
    • Application of methods to a subsample of the National Comorbidity Survey Replication Adolescent Supplement.

    Main Results:

    • Doubly robust estimators demonstrated superior performance over IPW regarding mean-squared error.
    • DR estimators maintained accuracy even when treatment, selection, or outcome models were misspecified.
    • Both DR estimators were found to be easy to implement for researchers.

    Conclusions:

    • Doubly robust estimators are recommended as a robust and practical alternative to IPW for ATE estimation in complex survey data.
    • These methods enhance the generalizability of treatment effect estimates from subsamples to target populations.
    • The study provides a framework for applied epidemiologists to improve the validity of their findings.