About Estimation Statistics

Statistical hypothesis test can be used to indicate whether the difference between two samples is due to random chance. but cannot comment on the size of the difference. A group of methods referred to as new statistics are seeing increased use instead of or in addition to p-values in order to quantity the magnitude of effects and the amount of uncertainty for estimated values.The group of statistical methods referred to as estimation statistics.

• Effect size methods involve  quantifying the association or difference between samples.
• Interval estimate methods involve quantifying the uncertainty around point estimations
• Meta-analysis involve quantifying the magnitude of an effect across multiple similar independent studies.

1.1 Tutorial Overview

1. Problems with Hypothesis Testing

2.Estimation Statistics

3. Effect Size

4. Interval Estimation

5. Meta-Analysis

1.2 Problems with Hypothesis Testing

Statistical hypothesis testing and the calculation of p-values is a popular way to present and interpret results.

1.3 Estimation Statistics

Estimation statisticas refer to methods that attempt to quantify a finding . This might include quantifying the size of an effect or the amount of uncertainty for a specific outcome or result.

Estimation statistics is a term to describe three main classes of methods . The three main classes of methods include:

• Effect Size: Methods for quantifying the size of an effect given a treatment or intervention.
• Interval Estimation. Methods for quantifying the amount of uncertainty in a value.
• Meta-Analysis. Methods for quantifying the findings across multiple similar studies.

1.4 Effect Size

The effect size describes the magnitude of a treatment or difference between two samples.Measuring the size of an effect is a big part of applied machine learning,and in fact, research in general.

There are two main classes of techniques used to quantify the magnitude of effects; they are:

• Association. The degree to which two samples change together
• Difference. The degree to which two samples are different.

1.5 Interval Estimation

Interval estimation refers to statistical methods for quantifying the uncertainty for an observation. Intervals transform a point estimate into a range that provides more information about the estimate, such as its precision, making them easier to compare and interpret.

There are three main types of intervals that are commonly calculated. They are:

• Tolerance Interval: The bounds or coverage of a proportion of a distribution with a specific level of confidence.

A tolerance interval may be used to set expectations on observations in a population or help to identify outliers.

• Confidence Interval: The bounds on the estimate of a population parameter.

A confidence interval can be used to interpret the range for a mean of a data sample that can become more precise as the sample size is increased.

• Prediction Interval: The bounds on a single observation.

A prediction interval can be used to provide a range for a prediction or forecast from a model.

1.6 Meta-Analysis

A meta-analysis refers to the use of a weighting of multiple similar studies in order to quantify a broader cross-study effect.Meta studies are useful when many small and similar studies have been performed with noisy and conflicting findings. Instead of taking the study conclusions at face value, statistical methods are used to combine multiple findings into a stronger finding than any single study.