#Reason and argument feldman answers how to

I recommend giving the two short essays to the students in advance (along with these instructions) so that class time is spent reconstructing the argument from Essay 1 and evaluating the objections from Essay 2. This discussion is best done in class in teams for 3-4. Discussion: Reconstruction + Evaluation Practice.Reading: Jonathan Fuller, вЂњWhatвЂ™s Missing in Pandemic Models,вЂќ Nautilus.

Reading: Richard Feldman, Reason and Argument, Pearson New International Edition -286.

Final Conclusion:В the target factors are (positively or negatively) correlated in the overall target population.

Representativeness Premise:В this says that the sample populations are representative of the target populations.

Target Correlation in Sample:В This says that the target factors are correlated in the sample.

Accuracy Premise:В this says that the factors measured accurately measure the target properties.

Measured Correlation in Sample:В a statement of the correlation between the measured factors in the sample population.

Results Obtained:В results can be expressed as several statistical statements reporting the rate at which the measured property was found in the two groups studied in the sample.

Final Conclusion:В approximately x percent of the target population has the target property.Д«ackground information:В Description of survey, including who or what was sampled, when, where, and how.

Representativeness Premise:В if x percent of the sample population has the target property, then approximately x percent of the target population has the target property.

Conclusion about Sample:В x percent of the sample population has the target property.

Accuracy Premise:В if x percent of the sample population has the measured property, then x percent of the sample population has the target property.

Result of Sample:В x percent of the sample population has the measured property.

So, this is a module for the COVID-19 pandemic, but it has significant applications beyond evaluating pandemic arguments.Д«y the end of this module, you will become familiar with reconstructing and evaluating arguments in the following two forms:Д«ackground information:В Description of sample: who or what was sampled, when, where, what they were asked or how they were observed, etc. making treatment decisions after diagnostic testing). Furthermore, these skills will help you navigate many personal decisions requiring interpretation of statistical data (i.e.

#Reason and argument feldman answers how to

Learning how to evaluate sample size, margin of error, and how to understand what it means to say that вЂњtwo factors are positively correlatedвЂќ is essential for engaging in a wide range of important live debates in our world. For example, you encounter this type of argument in attempts to predict the results of sports games, elections, or the future state of the economy.

Beyond the Pandemic - Broader Applications: Statistical arguments are everywhere.Apply this skill to the arguments we have encountered during the Covid-19 Pandemic.Reconstruct and evaluate Statistical and Correlation Standard Form Arguments (following the patterns explained below).As an introduction to this module, consider the examples of statistical reasoning on display in this article: A cognitive scientist explains Covid-19 death statistics, faulty causal reasoning, and the CDCвЂ™s 6%.Д«y the end of this module, you will be able to: Since March 2020, weвЂ™ve surely learned thereвЂ™s no escape from statistical arguments, both good and bad. InВ Session 2, we will focus on correlation arguments.ДЄlthough statistics courses may have a bad reputation, we avoid them to our detriment. InВ Session 1, we will focus on statistical survey arguments. This module will focus on two standard patterns of argument and common objections to each. This module will explore a method for reconstructing and evaluating standard statistical arguments.