Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

Parametric statistics is a branch of statistics that assumes sample data come from populations that are adequately modeled by probability distributions with a set of parameters.

your sample mean is unlikely to equal the population mean. The difference between the sample statistic and the population value is the sampling error.

Power analysis helps you manage an essential tradeoff. As you increase the sample size, the hypothesis test gains a greater ability to detect small effects. This situation sounds fantastic. However, larger sample sizes cost more money. And, there is a point where an effect becomes so miniscule that it is meaningless in a practical sense.

hypothesis testing builds on a broad range of statistical knowledge, such as inferential statistics, experimental design, measures of central tendency and variability, data types, and probability distributions

Because you can rarely measure an entire population, you usually don’t know the real value of a parameter. In fact, parameter values are almost always unknowable.

Descriptive statistics describe a sample. That’s pretty straightforward. You simply take a group that you’re interested in, record data about the group members, and then use summary statistics and graphs to present the group properties.

Confidence intervals incorporate the uncertainty and sample error to create a range of values the actual population value is likely to fall within.

hypothesis test evaluates two mutually exclusive statements about the population and determines which statement the data support.

On the probability distribution plot, the significance level defines how far the sample value must be from the null value before we can reject the null hypothesis.