Research Methods in Science UC LEADS Summer 2003 Lecture 1

Research Methods in Science: Outline of talk Overview of general principles of the scientific method Philosophy of science –examine objections Bayesian and frequentist approach Humanistic side of science Ethics in science (case studies) Scientific writing

What do you think the scientific method is?

Elementary Scientific Method Hypothesis formation Hypothesis testing Deductive and inductive logic Controlled experiments, replication, and repeatability Interaction between data and theory Limits to science’s domain

The Scientific Method (“mission statement”) The scientific method is the process by which scientists, collectively and over time, endeavor to construct an accurate (that is, reliable, consistent and non-arbitrary) representation of the world

General principles that pervade all of the sciences Hypothesis generation and testing Deductive and inductive logic Parsimony Science’s presuppositions, domains, and limits

Hypothesis generation and testing Formulation of a hypothesis to explain a phenomena “Educated guess” A hypothesis must be falsifiable

The Loch Ness Monster is alive and well The Loch Ness Monster does not exist There is life on Mars There is no life on Mars DNA is the genetic material of all life DNA is not the genetic material

Hypothesis Generation and Testing Based on my (or someone else’s) observations, I predict that: H 0 : no differences H A : significant difference } Treatments, controls, independent & dependent variables, etc.

} Let’s do an Experimental Test!!

Experimental Tests: What are the main features? Clear hypothesis Identify independent and dependent variables Assign controls Repeatable, hence verifiable results Used to support or refute claims

General principles that pervade all of the sciences Hypothesis generation and testing Deductive and inductive logic

Deductive and Inductive Logic (distinction #1) The conclusion of a deductive argument is already contained implicitly in its premises The conclusion of an inductive argument goes beyond the information in its premises

Deductive and Inductive Logic (distinction #2) Given the truth of all of its premises, the truth of an inductive argument’s conclusion follows with at most high probability Deduction argues from a given model’s general principles to specific cases of expected data

Deductive and Inductive Logic (distinction #3) Deduction argues from a given model’s general principles to specific cases of expected data Induction argues in the opposite direction, from actual data to an inferred model

Deductive and Inductive Logic One is based on statistics (inductive) The other is based on probability

Deductive and Inductive Logic (telling the difference) Given: A “fair coin” is one that gives tails with probability 0.5 and head 0.5. Problem 1: Given that a coin is a fair coin. What is the probability that the coin will produce 45 heads and 55 tails? Problem 2: Given that 100 tosses of a coin produce 45 heads and 55 tails. What is the probability that the coin is a fair coin?

Why is induction so pervasive and critical in science? Science is almost entirely about unobservables -- about things and times outside the database of actual observations. Iron melts at 1,535°C (but everywhere?) Water boils at 100°C (but everywhere?)

The basis of induction: Aristotle Aristotle ( BC) offered 3 methods of induction Unifying concept: in deductive arguments, which are composed of premises, inductive arguments are the scaffolds that raise the status of the deductive argument to a law- like status

The basis of induction: Aristotle Dialectical induction (Topics). Not entirely relevant to scientific research, but useful: –mentor to pupil discourse –“If a skilled pilot is the best pilot and the skilled charioteer is the best charioteer, then, in general, the skilled [person] is the best [person] in any particular sphere” (Perez-Ramos 1988)

The basis of induction: Aristotle Enumerative induction (Prior Analytics). Statements about individual objects provide the basis or premises for a general conclusion: –from observing numerous adult humans, an inductive argument could conclude that all humans have 32 teeth

The basis of induction: Aristotle Intuitive induction (Posterior Analytics). Direct intuition of the general principles exemplified in the data: –bright side of the mood always faces the sun, so the moon shines because of reflected sunlight

General principles that pervade all of the sciences Hypothesis generation and testing Deductive and inductive logic Parsimony

Shortest path or the less complex “explanation” to the “true state of nature” A B

Parsimony Keynes (1962) expressed parsimony as the law of the limited variety in nature –Iron melts at 1,535°C –unlimited nature…unique atoms…unique properties…no iron, oxygen, no humans (sum of the parts) –100 chemical elements –related presuppositions of induction

Parsimony The principle of parsimony recommends that from among theories fitting the data equally well, scientists choose the simplest theory. Thus, the fit of the data is not the only criterion bearing on the theory choice

Parsimony Additional criteria includes: predictive accuracy explanatory power testability fruitfulness in generating new insights and knowledge coherent with other scientific and philosophical beliefs repeatability of results

Parsimony Q: Why is parsimony an important principle in science?. A1: The entire scientific enterprise has never produced, and never will produce, a single conclusion without invoking parsimony A2: Economy…facilitate insight, improve accuracy, and increase efficiency

General principles that pervade all of the sciences Hypothesis generation and testing Deductive and inductive logic Parsimony Science’s presuppositions, domains, and limits

Set of beliefs that allow a person to validate her observations, results, conclusions (objectivity of science) –constancy of the universe –parsimony Acceptance and acknowledgement of the knowable and the unknowable

General principles that pervade all of the sciences Hypothesis generation and testing Deductive and inductive logic Parsimony Science’s presuppositions, domains, and limits

General principles that pervade all of the sciences Hypothesis generation and testing Deductive and inductive logic Parsimony Science’s presuppositions, domains, and limits… How do we represent this set of principles that found in all of the sciences?

General principles that pervade all of the sciences Hypothesis generation and testing Deductive and inductive logic Parsimony Science’s presuppositions, domains, and limits } SCIENTIFIC METHOD

General principles that pervade all of the sciences There are detractors of the idea that a scientific method, upon which we are able to make claims about the true state of nature, does not exist } SCIENTIFIC METHOD

General principles that pervade all of the sciences There are detractors of the idea that a scientific method, upon which we are able to make claims about the true state of nature, does not exist } Philosophical & Scientific

General principles that pervade all of the sciences Paul Feyerabend insisted that there are no objective standards of rationality, so naturally there is no logic or method to science…“anything goes” in science…it is no more productive of truth than “ancient myth-tellers, troubadours and court jesters” } Philosophical cannot

General principles that pervade all of the sciences Thomas Kuhn is critical of what he sees as modernist misrepresentation of the nature of science: Modernist definitions of science claim that science is objective because it is empirical (based only on the data of our senses), rational (reasonable, or logically defensible) and that its presuppositions are obviously true... } Scientific cannot

General principles that pervade all of the sciences Kuhn claims science is a social enterprise and as such is also quite subjective. He argues that, "every individual choice between competing theories depends on a mixture of objective and subjective factors." } Scientific cannot

General principles that pervade all of the sciences Instead, science occurs in revolutions where old ideas are thrown out and new ones accepted. Science is therefore capricious, and each discipline of science cannot share a set of pervading principles } Scientific cannot

General principles that pervade all of the sciences These revolutions are called PARADIGM SHIFTS } Scientific cannot

astronomy geology chemistry physics biology

astronomy geology chemistry physics biology General principles and technologies are distinct to each scientific discipline

Thought experiment You have been awarded a $500,000 grant and can spend it on any type of equipment that is relevant to your research. Make a list of what you will buy and justify it (don’t worry about EXACT price values as you essentially can afford almost anything!) (don’t forget about Gregorio’s research!)

Thought experiment Can you safely say that you will not rely on or utilize any of the following principles by using your new equipment?: hypothesis generation and testing Deductive and inductive logic Parsimony Science’s presuppositions, domains, and limits

“you” “them”

Pervasive in all sciences Based on Greek philosophers & many others Non-negotiable presuppositions of perception: “you see what I see…you feel as I feel”

} Common to all of us } Unique to our fields but after the same thing...

The Scientific Method (“mission statement”) The scientific method is the process by which scientists, collectively and over time, endeavor to construct an accurate (that is, reliable, consistent and non-arbitrary) representation of the world

Bayesian and Frequentist Approach to Scientific Research Bayesian Statistics have been developed for a variety of purposes, such as designing experiments, estimating the values of quantities of interest, and testing hypothesis Useful because this family of statistics takes into account prior results as opposed to assigning independence to each result, thereby introducing efficiency

Bayesian and Frequentist Approach to Scientific Research For a loaded dice (biased for “6”): The frequentist views dice throws as independent events, each number or face having an equal probability: each value has a 1/6 probability of appearing. The Bayesian, the probability of getting a “6” will be more than just 1/6 (as will the probability of being thrown out on your ear!)

Bayesian Approach to Scientific Research The search for patterns in data will be “more realistic” as you do not discard “prior” knowledge -- it helps you get to the “answer” much faster Calculations are not very difficult for small sample sizes, but can get complicated for large ones…let’s see an example:

Bayesian Example Coin toss determines the configuration of the marbles that go into an opaque urn: heads: place 1 white + 3 blue marbles (WBBB) tails: place 3 white + 1 marbles blue (WWWB) Only “coin-tosser” knows

Bayesian Example Ratio of the likelihood of “heads” to the likelihood of “tails”

Posterior probability Number of draws

Bayesian Approach to Scientific Research Your confidence in the results (and hence your hypothesis) increases tremendously with each draw of a marble If trials are expensive then using likelihood values are important Can be computationally complex (trade off)

The Humanistic side of Science Your perceptions of the humanistic side of science: It can lie between one’s research and one’s beliefs It may not be realized at the outset It may change during your career You may not want them to intermingle

Science as a Liberal Art The search for and the advancement of knowledge and truth is a common goal among scientists The “truth” will (hopefully) be used to improve the world in which we live in The “truth” will be used for just and moral purposes

Science as a Liberal Art As scientist, we may be in dilemmas that will challenge out personal beliefs A strong conviction in what one believes should reflect the kind of work one undertakes May or may not reflect current social climate

Science as a Liberal Art Examples of controversial research: stem cell research genetic engineering / GM food nuclear sciences control systems (used by the defense) biological control alternative fuel research

Science as a Liberal Art Do you have ethical “boundaries” that you have considered in your work?

Ethics Case Studies Isa and Senait will lead discussion Introduce the paper Break into groups Read and discuss paper Develop topics for big discussion Introduce second ethics issue (no break-out groups)

Ethics in Research Reporting of data accurately is seen not only as a high professional quality, but also a moral one. Why?

Ethics in Research Ethical researchers do not plagiarize or claim credit for the results of others; They do not misrepresent sources or invent results; They do not submit data whose accuracy they have reason to question, unless they raise the question; They do not conceal objections that they cannot rebut; They do not caricature or distort opposing views; They do not destroy or conceal sources and data important for those to follow

Research Ethics and Science Writing: Example

Concept Map: Water Example