Lab 1: Analyzing Scientific Data Oxnard College Chem R110 Name Evan Zellmer _ Section_________________ Critically Analyzing Scientific Data Activity adapted from Miller, D. M; Chengelis Czegan, D.A.Integrating the Liberal Arts and Chemistry: A Series of General Chemistry Assignments to Develop Science Literacy. J. Chem. Ed, 2016, 864-869 In today’s fast-paced, technology-driven, 24-hours news cycle society, it seems information is coming at you faster than you can keep up. Much of this information is “scientific” news, stories, or advances and can sound very legitimate…but that is not always the case. As an informed listener or reader, you need to be able to take in the information being presented, critically and objectively analyze it, and make your own judgement as to the validity of the conclusions that are being reported. In science there are many factors that affect whether a news source, website, or story is credible. This activity is designed to help you assess what information should be taken seriously versus information being hyped to help attain some other purpose. You will apply those skills to analyze some real-life, current scenarios. Hallmarks of Reliable Scientific Research When scientists observe some phenomenon that is interesting to them, they often use the scientific method to learn more about that process. The scientific process consists of the following steps: Observation → Hypothesis → Experiment(s) → Revise Hypothesis → More Experiments This process provides a framework to analyze new findings, but it does not ensure the validity of any findings. A valid scientific study is characterized by additional features that serve to challenge and scrutinize any new findings prior to publication. These characteristics are: 1. Representative samples (large n, reproducible data) 2. Reproducibility (control groups, cause vs correlation, biases & placebo effects) It is unavoidable that there will be some amount of variation in results from a scientific experiment. This arises from random error sources, which are outside of the control of the researcher. This could be changes in the environment (temperature fluctuations for example) or just the inability to exactly repeat precisely how you took a measurement. Random error affects scientific data’s reproducibility or precision : the ability to obtain the same result every time an experiment is performed. The good news about random error is that it usually averages out statistically (for every temperature fluctuation where it got a little warmer, there will likely be a fluctuation where it got a little cooler). If too few measurements are used to draw conclusions, random errors present in the experiments may be misinterpreted as true phenomena, and a scientist may draw unwarranted conclusions from the data. This means when you are hearing about a new scientific discovery, information, or finding, if it is unclear how many times the experiment was repeated, or even worse if it was clearly not repeated at all, or very small sample sizes were used, then you should be skeptical about the results.
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