Related Experiment Video

Updated: Aug 9, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

An evaluation of population forecast errors for Florida and its counties

S K Smith, R Bayya

    Applied Demography
    |April 1, 1992
    PubMed

    Keywords:
    Administrative DistrictsAmericasComparative StudiesCountiesDeveloped CountriesError SourcesEstimation TechnicsEvaluationFloridaGeographic FactorsMeasurementNorth AmericaNorthern AmericaPopulationPopulation ForecastPopulation ProjectionReliabilityResearch MethodologyStudiesUnited States

    More Related Videos

    Sampling for Estimating Frankliniella Species Flower Thrips and Orius Species Predators in Field Experiments07:13

    Sampling for Estimating Frankliniella Species Flower Thrips and Orius Species Predators in Field Experiments

    Published on: July 17, 2019

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    Related Concept Videos

    Estimating Population Mean with Known Standard Deviation01:16

    Estimating Population Mean with Known Standard Deviation

    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.
    The confidence interval estimate will have the form as follows:
    (point estimate - error bound, point estimate + error bound)
    The...
    Distributions to Estimate Population Parameter01:26

    Distributions to Estimate Population Parameter

    The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
    Estimating Population Standard Deviation01:26

    Estimating Population Standard Deviation

    When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
    Estimating Population Mean with Unknown Standard Deviation01:22

    Estimating Population Mean with Unknown Standard Deviation

    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 Guinness...
    Systematic Error: Methodological and Sampling Errors01:15

    Systematic Error: Methodological and Sampling Errors

    In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
    Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
    Random Error01:04

    Random Error

    Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...