Grasp how statistics can explore a population through a sample, with valid conclusions only if the sample represents the wider group. Recognise that a haphazard sample generally offers a representative selection, aiding solid assumptions.

Employ information from a random sample to make presumptions about a predominantly unknown population. Generate numerous duplicates of the same sized sample to measure the possible variance in forecasts. For instance, predict the school election winner or estimate the mean word size in a book.

Casually evaluate the visible overlap of two numerical data distributions with comparable variability, expressing the difference between centres as a multiple of variability magnitude. For example, consider mean heights of basketball and soccer players or other visually distinct distributions on a dot plot.

Use central tendency measures and variability measures for numerical data from random samples to casually compare two populations. For instance, compare word lengths from different grade's science books.

Comprehend that the likelihood of a random occurrence is represented as a number between 0 and 1, with higher values indicating higher chances.

Estimate the probability of a random incident by gathering data on the processes that lead to it and observing its long-term relative frequency. This is used to predict relative frequency