Million Dollar Data: Building Confidence - Vol. 1

Global warming, our current and greatest challenge, is without precedent. Among the many consequences that are impacting our society, one unanticipated concern involves scientific truth. When the President of the United States, and others in his administration, declare that global warming is fake science, it calls into question what real science is and what real school science should be. I will argue that real science is quality science, one that is based on the rigorous collection of reliable and valid data. To collect quality data requires bending over backwards to get things right, and this is exactly what makes science so special. Truth is made when scientists go this extra yard and devise controlled experiments, collect large data sets, confirm the data, and rationally analyze their results. Making scientific truth sounds difficult to do in the science laboratory, but in reality, there are many straightforward ways that truth can be constructed. In the first of two volumes, I discuss twelve such ways – I call them Confidence Indicators – that can allow students to strongly believe in their data and their subsequent results. Many of these methods are intuitive and can be used by young students on the late elementary level all the way up to those taking introductory college science courses. As in life, science is not without doubt. In the second volume I introduce the concept of scientific uncertainty and the indicators used to calculate its magnitude. I will show that science is about connecting confidence with uncertainty in a specific manner, what I refer to as the Confidence-Uncertainty Continuum expression. This important relationship epitomizes the scientific enterprise as a search for probabilistic rather than absolute truth. This two-volume set will contain a variety of ways that data quality can be instituted into a science curriculum. To support its use, many of the examples that I will present involve science teachers as well as student work and feedback from different grade levels and in different scientific disciplines. Specific chapters will be devoted to reviewing the academic literature on data quality as well as describing my own personal research on this important but often neglected topic.