# 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.

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- 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

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