British Columbia Institute of


LIBS 7006 - Philosophy of Science: Understanding Scientific Reasoning

Liberal Studies Part-time Studies Course

School of Computing and Academic Studies

Course Details

Develops simple, yet powerful methods for understanding and evaluating a wide variety of scientific and pseudo scientific material. Introduces some of the great thinkers and theories of the past, both winners and losers. Reflects on what makes scientific reasoning so effective, and uses these reflections to evaluate some contemporary criticisms of the place of science in society.


BCIT ENGL 1177, or 6 credits BCIT Communication at 1100-level or above, or 3 credits of a university/college first-year social science or humanities course.





Fall 2019

Below is one offering of this course for the Fall 2019 term.

CRN 33566

Tue Sep 03 - Fri Dec 13 15 Weeks

Class Meeting Times

Dates Days Times Locations
Sep 03 - Dec 13 Online



Course Outline

TBD – see Learning Outcomes in the interim




  1. Internet delivery format.
  2. *(A)* This is NOT a SELF-PACED course. There will be specific timelines for assignments and exams. Course content, kind and quality of assignments and general standards for this online course are the same as classroom courses. You will have discussions and assignments to complete each week (although you do NOT have to be online at a particular time or day). *(B)* FINAL EXAMS: All final exams MUST be written at BCIT during the last week of the course on the designated dates and times given at course start. If you live outside the Lower Mainland area you will be required to have an approved proctor administer the exam. You are directly responsible for any invigilation fees and related costs.
  3. Important course information will be sent to you prior to your course start date. Check your myBCIT email account to access this information.

Learning Outcomes

Upon successful completion, the student will be able to:

  • Distinguish theoretical models from real-world objects of study.
    • Abstract brief descriptions of theoretical models from popular or semi-technical science reports.
    • Recognize the empirical predictions of such models.
    • Note whether these predictions are actually fulfilled in observation or experiment (negative or positive evidence).
    • Judge whether there are other plausible models that can (also) explain the facts.
    • Judge whether to accept, reject or suspend judgement on whether the proposed model accurately represents the world.
    • Judge whether the proposed model is credible, but needs further development or testing; or judge that the proposed model is implausible, and (currently, at least) not worth pursuing.
  • See how historically eminent scientific models triumphed over then-plausible contemporary views.
    • The students will have read through James Watson's classic, The Double Helix, as well as the brief historical episodes discussed in the texts.
    • The students will also have been provided with further details regarding the historical episodes discussed in the text. Such nuances can help reveal how and when theory evaluations are tied to theoretical traditions.
  • See that 'marginal science' models are generally not worth accepting or pursuing because (i) they make vague or multiple predictions; (ii) their predictive 'successes' are also explained by more plausible models; or (iii) they are deeply inconsistent with well-established theories.
    • Recognize and intelligently engage with some alternative philosophies of science.
  • Distinguish sample and statistical models from the larger population they are designed to represent.
    • Understand statistical proportions, distributions, correlations and variables.
    • Understand basic mathematical probability models (addition, multiplication rules, conditional probabilities, the structure of random sampling, and standard deviation).
    • Quickly compute margins of error from sample sizes.
    • Recognize the connection between margin of error and confidence level.
    • Construct simple statistical models of reported proportions, distributions and correlations.
    • Recognize and evaluate samples according to how well they approximate random sampling.
    • Distinguish statistical significance from 'significance.'
    • Judge whether a proposed statistical model should be accepted, rejected or treated as unsupported.
  • Distinguish causation from correlation.
    • Distinguish deterministic from probabilistic models of causation.
    • Distinguish causal models for individuals from those for populations.
    • Understand causal 'effectiveness.'
    • Understand the role of control and experimental groups in establishing causal hypotheses.
    • Understand the theoretical superiority of Randomized Experimental Designs for supporting causal hypotheses.
    • See when Prospective and Retrospective causal models are required, and when they can support a causal hypothesis.
    • Understand controlling for other variables, matching control and experimental groups, and constructing control groups (in Retrospective studies).
  • (If time permits.) Understand the elements of decision-making models (options, states of the world, outcomes and values).
    • Distinguish ranked from measured values.
    • Recognize when a situation calls for decision making with certainty, uncertainty, or risk.
    • Grasp better, worse and satisfactory options and correlated strategies.
    • Combine probabilities and values, in terms of expected values in situations involving known risks.

Effective as of Fall 2003

Related Programs

LIBS 7006 is offered as a part of the following programs:

School of Business

  1. Accounting
    Full-time/Part-time   Bachelor of Accounting
  2. Bachelor of Business Administration
    Full-time/Part-time   Bachelor of Business Administration

School of Health Sciences

  1. Bachelor of Health Science (Magnetic Resonance Imaging Option)
    Part-time/Distance & Online Learning   Bachelor of Health Science
  2. Food Technology and Operations Management
    Full-time/Distance & Online Learning   Bachelor of Science


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