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What are Estimates?

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates.
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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 Guinness...

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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.
The confidence interval estimate will have the form as follows:
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Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

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Related Experiment Video

Updated: May 14, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Published on: March 25, 2014

Estimation of a mean template from spike-train data.

Wei Wu¹, Anuj Srivastava

¹Department of Statistics, Florida State University, Tallahassee, FL 32306, USA. wwu@stat.fsu.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

This summary is machine-generated.

Researchers developed a new model-free algorithm to compute the mean spike train, offering insights into neural coding. This method accurately captures typical firing patterns and improves decoding of motor behaviors from neural recordings.

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Area of Science:

Computational Neuroscience
Neural Coding
Data Analysis

Background:

Probabilistic and statistical methods are common for analyzing neural activity but rely on model assumptions.
Existing methods often focus on time-dependent neural activity, limiting their scope.
A model-free, metric-based approach is needed to analyze spike trains directly.

Purpose of the Study:

To develop an efficient and convergence-proven algorithm for computing the mean spike train.
To establish a data-driven, model-free method for analyzing neural firing patterns.
To define the mean spike train in a function space for direct analysis.

Main Methods:

A metric-based approach analyzing the space of spike trains directly.
Development of an efficient algorithm with proven convergence.
Viewing spike trains as individual points in a function space.

Main Results:

An efficient algorithm for computing the mean spike train was developed.
The algorithm successfully captured typical spike train patterns in primate motor cortex recordings.
The estimated mean spike trains demonstrated accurate and efficient decoding of motor behaviors.

Conclusions:

The novel model-free algorithm provides a robust method for analyzing spike trains.
This approach enhances understanding of neural coding and firing patterns.
The method has practical applications in decoding motor behaviors from neural data.