Presentation on theme: "Science 1101 Science, Society, and the Environment I Instructor Valerie King."— Presentation transcript:
Science 1101 Science, Society, and the Environment I Instructor Valerie King
Lecture 1 Outline I. What is Science? A. Forms of Scientific Inquiry B. Types of Logic II. Scientific Design A. Scientific Method B. Theory and Laws III. Scientific Method in Action A. Examples B. Statistics
What is Science? “Science” derived from Latin ‘to know’ Way of asking and answering questions Seeking answers to questions about natural phenomena (we are therefore limited to what kinds of questions we ask) Scientific thinking reduces emotional reactions
Forms of Scientific Inquiry Discovery or Descriptive Science – Observation – Qualitative vs. Quantitative data
Types of Logic Inductive Reasoning – Derive generalizations based on specific observations
Types of Logic Inductive Reasoning – Derive generalizations based on specific observations Deductive Reasoning - Specific predictions follow from general premise
Forms of Scientific Inquiry Discovery or Descriptive Science – Observation – Qualitative vs. Quantitative data Hypothesis-Based Science
Scientific Design Scientific knowledge begins with an observation and a proposed explanation. Explanation called a hypothesis A hypothesis is testable and falsifiable In science hypotheses are tested by using them to make predictions about how a particular system will behave
Example Hypothesis: all objects fall when dropped – Test this by dropping objects – Each object we drop is a test of our prediction, the more successful tests the more confidence in our hypothesis
What if we drop a helium balloon? What if we drop something in the space shuttle in space? These are clear exceptions to our original hypothesis-does this make our hypothesis invalid?
Theories and Natural Laws Theory: a description of the world that covers a relatively large number of phenomena and has met many observational and experimental tests Law of Nature: theory (or group of theories) that has been tested extensively and seems to apply everywhere in the universe-they become part of the conceptual framework of a particular field
Scientific Method in Action We use the scientific method in everyday life Example: You got in your car to drive up here and turned the key but the car wouldn’t start (observation)
Scientific Method in Action Example: You got in your car to drive up here and turned the key but the car wouldn’t start (observation) Hypothesis: There is something wrong with the car
Scientific Method in Action Example: You got in your car to drive up here and turned the key but the car wouldn’t start (observation) Hypothesis: There is something wrong with the car Predictions: battery dead, ignition problem, out of gas
Scientific Method in Action Test predictions: turn on headlights, check spark plug wires, dip stick in gas tank
Scientific Method in Action Test predictions: turn on headlights, check spark plug wires, dip stick in gas tank Analyze results: headlights work, strong ignition spark, no gas on dip stick-gas gauge reads half full
Scientific Method in Action Test predictions: turn on headlights, check spark plug wires, dip stick in gas tank Analyze results: headlights work, strong ignition spark, no gas on dip stick-gas gauge reads half full Draw conclusion: gauge inaccurate, out of gas
Scientific Method in Action I want to market a new flea collar for dogs that is a natural remedy-no harsh chemicals. But first I have to see if it really works. Hypothesis: King’s collar repels fleas Prediction: dogs wearing the King collar will have fewer fleas than dogs not wearing the King collar
Important terms: Independent (manipulated) variable: condition or event under study (choose 1) Dependent (responding) variable: condition that could change under the influence of the independent variable (measure this) Controlled variables: conditions which could effect the outcome of the expt so they must be held constant between groups.
experimental group: group(s) subjected to the independent variable control group: group not subjected to the independent variable, used as measuring stick reproducibility: producing the same result consistently to verify result. It is therefore important to describe your experimental design in enough detail for others to perform the same experiment.
Let’s recap Hyp: King’s collar repels fleas Pred: dogs wearing King’s collar will have fewer fleas than those without collar IV: King’s collar DV: presence of fleas CV’s: anything that might effect the number of fleas on the dogs Can we think of some???
Experimental Design Obtain 500 dogs of various breeds from local shelters. Have vet weed out the 200 dogs with the most fleas. Randomly assign individuals to 2 groups. Board the dogs in identical environments and treat them the same except that one group gets to wear the King collar and the other group does not After 2 wks. The dogs are examined by a vet for fleas.
Results: the dogs wearing the King collars were virtually free of fleas after the 2 wk period compared to the dogs without the collars which had about the same number of fleas as when the experiment began
Second Example Observation: polar bears are white, you wonder why this is so Hypotheses: Match arctic landscape for protection/predation White fur may reduce heat loss in warm-blooded animals Maybe polar bears are unable to produce melanin therefore they are white as result
Prediction: polar bears which are white will capture more prey than those which are dark –IV: color of bears –DV: number of prey Experimental Design: spray paint 5 polar bears dark(experimental Group)/leave 5 white(control group) Track prey capture for 8 weeks
Effect of Polar Bear coat color on prey capture Color of BearWhiteDark Number of Prey127
Probability and Statistics Probability: an attempt to measure and predict the likelihood of an event Statistics: allow you to evaluate comparisons between experimental and control groups
Effect of Polar Bear coat color on prey capture Color of BearWhiteDark Mean Number of Prey captured 127 Mean: sum the values, divide by the number of values
Assumptions: factors thought to be true for the investigation but have not been verified or controlled –Commonly accepted information –Thought to be held constant but not controlled –Factors beyond the investigators control because of technical or time considerations Incorrect assumptions invalidate an experiment!
Assumptions: 1. All of the bears are equally hungry 2. Spray painting the bears has no effect on their behavior etc. accept to make them stand out on the ice 3. Our sample of bears is a good representation of the polar bear population in general
Statistics Sample Size: # of observations necessary to have a reliable representation of a population Confidence Limits: estimates that reflect the reliability of your mean (average) – Probability your sample is similar to other random samples of that population