Understanding Inferential Statistics

Inferential statistics allow researchers to draw conclusions that extend beyond the immediate data.
They help in making inferences about populations using data drawn from a sample.
Unlike descriptive statistics, which simply describe data, inferential statistics test hypotheses and make predictions about future occurrences.

Key Statistical Tests

The t-test is used when the data is interval or ratio level and the aim is to compare the means of two conditions or groups.
Chi-square is useful when dealing with nominal or categorical data. It shows if there’s a significant association between two variables.
Analysis of Variance (ANOVA) is used when comparing the means of more than two groups.
A correlation test is used to assess the relationship between two variables.

The Null Hypothesis states that there would be no effect or relationship in the population. It is the hypothesis that is tested for possible rejection.
The Alternative Hypothesis states that there would be an effect or relationship.
Inferential statistics provide a p-value, which indicates the probability that the results occurred by chance.
A p-value of less than 0.05 is often considered significant. It suggests there’s less than a 5% chance that the data occurred by chance alone.

Inferential statistical tests give a direction to the conclusion, allowing researchers to accept or reject the null hypothesis.
They allow for generalisation of results from a sample to a population.
Keep in mind possible errors. A Type I error is rejecting the null hypothesis when it is true. A Type II error is failing to reject a false null hypothesis.
However, remember that statistical significance does not imply practical significance. The results you get must have real-world meaning and application.