Statistics: methods/procedures for building experiments, obtaining data, then organizing, summarizing, analyzing, interpreting, presenting, and drawing conclusions based on the data. What makes a good sample: sampling designs. In lieu of taking a census (collecting data from everything in population) Sample size big enough to capture variability (so can account for variability) Ex. we were overestimating mean of word length (doesn(cid:495)t mean everyone. Selection bias: systemic favouritism in the data selection process, leading to misleading results overestimated! Sampling error: difference between a sample statistic and the true population parameter, due to chance sample differences. Random sampling: each member of the population has an equal chance of being selected. Simple random sampling (srs): each possible sample of a required size (n) has an equal chance of being selected: all combinations of size n are equally possible, no restrictions on sample composition. The best and easiest method of getting a representative sample.