REASONING ABOUT INFORMAL STATISTICAL<br>INFERENCE: ONE STATISTICIAN'S VIEW

This paper identifies key concepts and issues associated with the reasoning of<br>informal statistical inference. I focus on key ideas of inference that I think all students<br>should learn, including at secondary level as well as tertiary. I argue that a<br>fundamental component of inference is to go beyond the data at hand, and I propose<br>that statistical inference requires basing the inference on a probability model. I<br>present several examples using randomization tests for connecting the randomness<br>used in collecting data to the inference to be drawn. I also mention some related<br>points from psychology and indicate some points of contention among statisticians,<br>which I hope will clarify rather than obscure issues.