IF-AT 1: Experimental Design + Kidney

Question 1

osmolarity vs osmolality

Answer 1

• osmolarity is particles/L
• osmolality is particles/kg

*same in urine because 1L water is 1kg and urine is 95% water!

Question 2

components to good research questions

Answer 2

• feasible
• interesting
• novel
• ethical
• relevant

Question 3

types of controls

Answer 3

• negative: ensure no change in DV when no change is expected
• positive: ensure change in DV when change is expected

Question 4

internal and external validity

Answer 4

• internal validity: conclusions are true and related to study findings (achieved by good design and valid results)
• external validity: conclusions apply to a population of interest (achieved by sample similar to population of interest)

Question 5

random and systematic error

Answer 5

• random: natural variation between measurement and true value, non-directional, reduced by increasing sample size
• systematic: error in design of experiment, directional error, increasing sample size won’t fix it (e.g. faulty equipment)

Question 6

null vs alternative hypothesis

Answer 6

• null: there is no relationship between variables
• alternative: there is a relationship betwen variables

Question 7

using p-values

Answer 7

• helps determine if control and treatment data sets are really different
• p-value: probability of observed result assuming null hypothesis is true
• if p <0.05 there is a 95% chance that the values are different

Question 8

type I and type II errors

Answer 8

• type I: incorrectly rejecting null hypothesis
• type II: incorrectly failing to reject the null hypothesis

Question 9

assumptions, limitations, and inferences

Answer 9

• assumptions: things we assume are true
• limitations: potential weaknesses–who conclusions are true for (sample population)
• inferences: what you decide is true based on data

Question 10

steps in scientific method

Answer 10

1. generate testable question
2. gather data and resources
3. form a hypothesis
4. collect new data
5. analyze data
6. interpret data and existing hypothesis
7. publish
8. verify results

Question 11

criteria for causality

Answer 11

• strength of association: statistical values
• consistency: observed in diff studies and populations)
• specificity: single factor influences a specific outcome
• temporality: factor precedes outcome
• biological gradient: outcome increases with increasing dose/exposure
• plausibility: biological explanation
• coherence: should not fundamentally contradict present knowledge
• experiment: based on randomized experiments
• analogy: effect has been shown for similar factors