1. What are four common mistakes made when interpreting inferential statistics?

1. What are four common mistakes made when interpreting inferential statistics? What would you do to ensure you did not make those mistakes in interpreting your findings?
2. 
Describe the purpose of inferential statistics. Give an example where sample size may affect type 2 error when evaluating social service programs. 
There is a “logic” behind inferential statistics, with inferential statistics positioning researchers to generalize study findings from a sample to a larger population of interest. By studying a smaller group of people, we can discover patterns that may say something about the population from which the sample was drawn. If researchers randomly select a sample of study participants from a larger population (using a sampling frame), then this strengthens external study validity, giving us greater confidence that the patterns we observe in the smaller sample will apply to the larger target population.
Inferential statistics can also be used to compare outcomes of study participants who receive a treatment or program with study participants who don’t receive the treatment or program (the control group). If study participants are randomly assigned to either the treatment or control group, this, by definition, means the study design is experimental. If they are not randomly assigned to treatment conditions, then the study design is quasi-experimental. Random assignment of participants to treatment (intervention) conditions strengthens internal study validity, (i.e. the extent to which we can make accurate inferences that the independent variable (i.e. the treatment, program, or intervention) causes (or leads to) outcomes (the dependent variables).   
the following website/online text called the Research Methods Knowledge Base, It is a user-friendly learning tool, developed by William Trochim. It is a wonderful resource to help you learn about research and statistical terms and concepts. 
https://conjointly.com/kb/