Sampling and Estimation

15 lessons0%

Lessons

Simple Random Sampling and Sampling Distribution

Sampling Error

Stratified Random Sampling

Time Series and Cross Sectional Data

Central Limit Theorem

Standard Error of the Sample Mean

Parameter Estimation

Point Estimates

Confidence Interval Estimates

Confidence Interval for a Population mean, with a known Population Variance

Confidence Interval for a Population mean, with an Unknown Population Variance

Confidence Interval for a Population Mean, when the Distribution is Non-normal

Student’s t Distribution

How to Read Student’s t Table

Biases in Sampling

Quizzes

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Point Estimates

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Parameter Estimation

In statistics, statistical inference refers to drawing conclusion based on the data. Statistical inferences are drawn in two broad ways, namely, hypothesis testing, and parameter estimation.

In hypothesis testing, we make a hypothesis and then we determine whether the sample data supports the hypothesis or does not support it. The hypothesis could be something like - Population mean is equal to 10. Then based on our sample data, we either accept or reject the hypothesis.

In contrast with hypothesis testing, under parameter estimation we try to estimate the population parameter by making use of the information available in the sample.

There are two types of parameter estimators: point estimates and confidence interval estimates.