Lecture Scientific (H-D) Method. Hypotheses come in two flavors:

Transcription

1 Lecture Scientific (H-D) Method I. Hypotheses A. Definitions B. Wildlife Examples C. Abstraction with cards II. Predictions A. H s & P s as foundation for Introductions B. Deductive logic practice (make predictions) Hypotheses come in two flavors: A hypothesis can be a potential explanation for an observed (known) natural phenomenon. A hypothesis can be a biological assertion for some unknown wildlife information (e.g., a solution to a management problem). potential explanation for natural phenomenon: Phenomenon: Mammals body size increases toward the poles (Bergmann s rule). potential explanation for natural phenomenon: Phenomenon: Mammals body size increases toward the poles (Bergmann s rule). Hypothesis: A larger body size facilitates more efficient thermoregulation (surface area to volume ratio). biological assertion for unknown wildlife information: Unknown info: Mate fidelity in northern flying squirrels. biological assertion for unknown wildlife information: Unknown info: Mate fidelity in northern flying squirrels. Hypothesis: Northern flying squirrels are monogamous within a breeding season. 1

2 What about a real management issue? the so-what factor looms large! potential explanation for natural phenomenon: Phenomenon: Waterfowl abundance on refuge has declined. wildlife potential explanation for natural phenomenon: Phenomenon: Waterfowl abundance on refuge has declined. wildlife Hypothesis: Water ph has declined and diminished submerged vegetation (duck food). biological assertion for unknown wildlife information: Unknown info: The value of old-growth redwood for Ensatina salamanders. Predictions are biological assertion for unknown wildlife information: Unknown info: The value of old-growth redwood for Ensatina salamanders. Hypothesis: Old growth redwood forest is better habitat than mature second-growth redwood forest for Ensatina salamanders. deductions (logical extensions) of research hypotheses. They are NOT (necessarily) what you think will happen. They are what must happen IF the hypothesis is true. 2

3 Predictions are deductions (logical extensions) of research hypotheses. Predictions are deductions (logical extensions) of research hypotheses. The best predictions are both necessary and sufficient to support their hypotheses. They are often phrased as if then statements Basic example: Hypothesis: Polly is a bird. (an assertion) Prediction: If Polly is a bird, then Polly has feathers. Basic example: Hypothesis: Polly is a bird. (an assertion) Prediction: If Polly is a bird, then Polly has feathers. Antecedent (repeat of hyp.) Consequent Is this prediction necessary for the hyp to be true? Is this prediction sufficient to support the hyp? Examine necessity by substituting must for then & make the statement a question. If Polly is a bird, then Polly has feathers? must YES = Necessary prediction If Polly is a bird, then Polly quacks? must NO = Unnecessary prediction Examine sufficiency by swapping antecedent & consequent. If Polly is a bird, then Polly has feathers. If Polly has feathers then she s a bird. YES = Sufficient prediction If Polly is a bird, then Polly is warm-blooded. If Polly is warm-blooded then she s a bird. NO = Insufficient prediction 3

4 If Polly is a bird, then Polly has feathers. Necessary & sufficient Remember Predictions are logical extensions (deductions) of hypotheses. If Polly is a bird, then Polly is warm-blooded. Necessary but insufficient If Polly is a bird, then Polly quacks. Unnecessary but sufficient Remember Predictions are logical extensions (deductions) of hypotheses. Q Hypothetico-Deductive Method They are not necessarily what you think will happen. They are what must happen if the hyp is true. They are often phrased as If then statements. The best predictions are both necessary and sufficient to support their hypotheses. True False 4

5 True 6 5

6 6 6 Possible explanations: (a) red & black alternate (b) even values (c) (a) and (b) Possible explanations: (a) red & black alternate (b) even values (c) (a) and (b) 6 Lessons of card game: 1. Experiments are more efficient than observations. Possible explanations: (a) red & black alternate (b) even values (c) (a) and (b) 8 J 2. Hypotheses can be tested by attempting falsification or confirmation. 3. When you have competing (alternative) hypotheses, scrutinize them until they reveal contrasting predictions. When you have competing (alternative) hypotheses, scrutinize them until they reveal contrasting predictions. If a prediction is upheld, its hypothesis is supported. If a predictions is not upheld, its hypothesis is rejected (or not supported). 6

7 Introductions encapsulate study design If a prediction is upheld, its hypothesis is supported. If a predictions is not upheld, its hypothesis is rejected (or not supported). Our studies should be designed to effectively test predictions & hypotheses. Previous Class Project Hypotheses Spring 2000 Phenomenon/Pattern: The number of birds at Klopp Lake varies from hour to hour. Previous Class Project Hypotheses Spring 2000 Phenomenon/Pattern: The number of birds at Klopp Lake varies from hour to hour. # birds time Potential Explanation: H: The number of birds varies at Klopp Lake due to changes in tide height. Number of Shorebirds Previous Class Project Hypotheses Spring 2003 Unknown wildlife information (problem): Factors affecting duck nest predation at the Arcata Marsh are poorly understood. Biological Assertion: H: Duck nest predation is related to amount of lateral and vertical cover. Nest Tide Height (in meters) Abundance of shorebirds at Klopp Lake as a function of tide height (m). Spearman rank correlation: R = 0.79, d.f. = 46, P <

8 7 Pattern described! Lateral cover score Successful nests Unsuccessful nests Figure 2. The mean lateral cover (±1SE), as measured with a Robel pole, was significantly higher at successful than at unsuccessful nests (t = 2.24, df = 84, P = 0.03). Raccoons broke eggs Ravens removed eggs 8