Title: Inferential Statistics - Overview, Parameters, Testing Methods
Headings: Inferential Statistics
Inferential Statistics
What is Inferential Statistics?
Summary
Population Parameters, Sample Statistics, Sampling Errors, and Confidence Intervals
Hypothesis Testing
Other Testing Methods
More Resources

Content: Point estimates and confidence intervals can be used in combination to produce better results. Every confidence interval is accompanied by a confidence level, which indicates the probability of the interval. A 95% (percent) confidence interval shows that if the same study is conducted numerous times with a completely new sample each time, it is likely that 95% of the studies will have an estimate that lies within the same range of values. It applies to estimates and not necessarily to parameters. To know more about different statistics concepts, check out CFI’s Statistics Fundamentals course! Hypothesis Testing
Hypothesis testing makes use of inferential statistics and is used to analyze relationships between variables and make population comparisons through the use of sample data. The steps for hypothesis testing include having a stated research hypothesis (null and alternate), data collection per the hypothesis test requirements, data analysis through the appropriate test, a decision to reject or accept the null hypothesis
Null Hypothesis The null hypothesis states that there is no relationship between two population parameters, i.e., an independent variable and a dependent
, and finally, a presentation and discussion of findings made. Hypothesis testing falls under the “statistical tests” category. Statistical tests account for sampling errors and can either be parametric (includes assumptions made regarding population distribution parameters) or non-parametric (does not include assumptions made regarding population distribution parameters). Parametric tests tend to be more trusted and reliable because they enable the detection of potential effects.