Scientific Process #4

Bio lecture #4

3 big federal funding bodies in canada

• Natural sciences & engineering research council (NSERC)

• Social sciences and humanities research council (SSHRC)

• Canadian institutes of Health research (CIHR)

Observational experiment

• “Natural experiment”

• Not in control of all variables 

• Does not manipulate conditions

• Common in ecology bc cant manipulate / make conditions perfectly standardized in nature

Controlled experiment

• “manipulative experiment” 

• In total control of each variable and can manipulate how all variables interact 

• Includes a “control group” that has no variables acting on it

Descriptive study 

• scientific work where you are not comparing or manipulating variables, ONLY describing a phenomenon, system, or observation

  • Sets the foundation for future experimental studies by establishing context and characteristics

Confounding factors

• variables that are outside the scope of your experiment, but are influencing the effect on your measured variables

Quantitative data

• data qualities that you can count or measure exactly and objectively 

  • ex) number of attacks, time until attack, percentage of brown hairs

  • Can either be continuous or discrete

continuous data 

• data that can be measured in any value possible, including fractions or decimals

ex) temperature was 34.5C during the experiment

Discrete data

• data that can be measured in integer or whole numbers 

• ex) 5 mice were attacked

Qualitative data

• Data qualities that have subjectivity about them, often interpretive 

  • ex) the color of mouse, behavioral response to attack, feelings about science

  • Is often put into categories for analysis

categorical data

• data that allows for some standardization of protocols and enables comparison without needing numerical values

sample size

• the selection of observations or tests you can accomplish

  • Ideally large enough to enable confidence that your observations are statistically representative of the larger population

replication

• the number of times you rerun your experiment or measure your variables to establish confidence that your samples are indeed statistically representative of the population

statistics 

• enables us to understand what differences we observe are truly representative of an effect in our experiment, and which are just random noise 

  • Use statistics to help us decide which hypothesis should be rejected

significant result

• when the results of an experiment are not thought to be random / something influenced the experience in a way that it can be confidently concluded that the results are “real” (this is good for supporting the hypothesis) 

  • Results of an experiment ARE NOT RANDOM

reproducibility

• if an experiment has significant result then you should be able to run the experiment again in the same way and arrive at the same conclusions and relationships

  • Central tenet of science 

  • “If ur results are real, i should be able to follow your methods and reproduce the same relationships between variables for myself”

peer-reviewed 

• sharing your scientific work through Peer-reviewed journal articles so others can read, assess, test, and ideally confirm results independently 

  • FINAL STEP in the SCIENTIFIC PROCESS