- 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
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.