How to use inferential statistics to evaluate sample data. Probability in statistical inference, meaning of statistical significance. T-test and the difference between one-tailed vs. two-tailed tests. Factors influencing probability of a type ii error. Ways of describing results of study: descriptive statistics, graphing techniques. Can also use inferential statistics to draw general conclusions. Inferential statistics let researchers: assess how confident they are that the results reflect what is true in the larger population, assess likelihood that the findings will occur if study was repeated over and over. Research findings are often based on sample data, but we want to make statements about populations. Want to see if the difference in sample means reflects a true difference in population means. Ex: in a survey, 57% prefers a; 43% prefers b. Results accurate within 3% points with 95% confidence level. This means: very (95%) confident that the entire population rather than a sample, 60-54% prefers.