###### Big Data

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A point estimate gives statisticians a single value as the estimate of a given population parameter. For example, the sample mean X̄ is the point estimate of the population mean μ. Similarly, the sample proportion *p *is a point estimate of the population proportion ** p** when binomial modeling is involved.

A point estimate is a specific outcome that takes a single numerical value. It has two main characteristics:

- a point estimate is a single numerical value specific to a given sample; and
- it has no sampling distribution.

Point estimates are subject to bias, where the bias is the difference between the expected value of the estimator and the true value of the population parameter involved. Each point estimate has a well-defined formula used in its calculation. Statisticians use the method of maximum likelihood or the method of moments to find good unbiased point estimates of the underlying population parameters.

We design a confidence interval estimate such that there is a range (lower confidence limit and upper confidence limit) within which analysts are confident that a population parameter lies. A probability is assigned indicating the likelihood that the designed interval contains the true value of the population parameter.

There are 3 parts that collectively form an interval estimate:

- confidence level;
- a statistic; and
- a margin of error.

A confidence interval has the lower and upper limits which serve as the bounds of the interval. The level of confidence highlights the uncertainty associated with samples and sampling methods. The precision of an interval estimate depends on the sample statistic and the margin of error. The interval estimate appears as: sample statistic ± margin of error.

Suppose we compute an interval estimate with 90% confidence level, what would this mean?

We could interpret this to mean that if we were to draw multiple samples using the same sampling method and then compute different interval estimates, we would expect 90% of the constructed intervals to contain the true value of the population parameter.

The margin of error is the range of values below and above the sample statistic.

For example, suppose pollsters predict that an independent candidate will receive 25% of the votes cast in an election and the survey has a margin of error of 5% at the 95% confidence level. How do you interpret this?

It means we are 95% confident that the independent candidate will receive between 20% and 30% of the votes cast.

In conclusion, the two concepts are very useful in hypothesis testing and overall statistical analysis.