HUMAN BEHAVIOUR WK2 WEB+SLIDE

Information Overload

The current era presents an abundance of information, but its quality varies significantly (Page 3).

Trustworthiness of Information

Evaluating the reliability of information is crucial, especially with the prevalence of 'fake news,' urban myths, and misleading content (Page 3).

Science as a Method

Science is a systematic approach using observable data to explain and understand the world in a trustworthy manner (Page 3).

Goal of Scientific Thinking

To inform our knowledge and create theories based on scientific research (Page 4).

Hypotheses in Everyday Life

Everyday statements are often hypotheses about how the world works (Page 4).

Scientific Claims

These are more likely to use less certain language and are associated with probabilities (Page 4).

Induction

Drawing general conclusions from specific observations; both scientific and everyday reasoning employ this (Page 4).

Example of Induction

Forming an opinion about cramming based on personal experience versus research studies (Page 4).

Need for Distinctions

Appreciating the differences between personal opinions and scientific statements is crucial for making informed decisions (Page 4).

Falsifiability

A key feature of scientific claims, meaning they can be conceivably demonstrated to be untrue (Page 5).

Karl Popper

Suggested that science can be distinguished from pseudoscience because scientific claims are capable of being falsified (Page 5).

Testable Claims

Scientific claims should be testable; 'falsifiable' essentially means 'testable' (Page 5).

Non-Falsifiable Claims

Claims that cannot be tested or disproven are unscientific (Page 5).

Popper's Critique of Freud

Popper was critical of Freud's explanations for mental illness because they lacked falsifiability (Page 5-6).

Importance of Falsification

Popper argued that non-falsifiable statements block scientific progress (Page 6).

Solution

Articulate the kinds of evidence that will disprove a hypothesis beforehand (Page 6).

Systematic Testing

Scientists systematically test potential causes to establish a comprehensive understanding (Page 7).

Ruling Out Bad Claims

Investigating alternative explanations is essential to rule out incorrect claims (Page 7).

Test Yourself 1

Examples of hypotheses and consideration of data to demonstrate if a statement is untrue (Page 7).

Modern Use of Falsification

Scientists are also interested in providing descriptions and explanations for the way things are (Page 7).

Hypothesis

A specific prediction based on previous research or scientific theory (Page 7).

Possible Outcomes

Considering all possible outcomes of a study and how to interpret them (Page 8).

Evidence vs. Proof

Results offer evidence in support of a hypothesis but do not prove it (Page 8).

Inductive Reasoning

Based on probabilities, making science better at shedding light on likelihood than proving something (Page 8).

Meteorology Example

Using inductive reasoning to create weather forecasts (Page 8).

Deductive Reasoning

Starts with general principles applied to specific instances; when premises are true and the argument is valid, the conclusion is proven (Page 9).

Deductive Truth

Must apply in all relevant circumstances (Page 9).

Complexity of Psychological Phenomena

Involves many contributing factors, making broad statements with certainty nearly impossible (Page 9).

Test Yourself 2

Examples to differentiate between inductive and deductive reasoning (Page 9).

Opposite Results

Researcher must admit the evidence does not support the hypothesis (Page 9).

Single Study Limitations

A single study should not outweigh the conclusions of many studies (Page 9).

No Difference in Results

Researcher must admit that she has not found support for her hypothesis (Page 10).

Quality of Observations

Interpreting results rests on the quality of the observations from which those results are drawn (Page 10).

Anecdotal Evidence

Limited by the quality and representativeness of observations and memory shortcomings (Page 10).

Well-Designed Research

Relies on observations that are systematically recorded, of high quality, and representative of the population it claims to describe (Page 10).

Null-Hypothesis Significance Testing (NHST)

Assesses the probability that the collected data would be the same if there were no relationship between the variables in the study (Page 11).

Research Example

Examining the relationship between student age and academic performance (Page 11).

Correlation

Measure of the relationship between two variables (Page 11).

NHST Goal

To test the probability that the researcher would find a link between age and class performance if there were, in reality, no such link (Page 11).

Null Hypothesis

A statement that two variables are not related (Page 12).

Alternative Hypothesis

A statement that two variables are related (Page 12).

Evaluating Hypotheses

Comparing what is expected (probability) with what is actually found (collected data) (Page 12).

Distribution of Data

The spread of values; comparing observed distributions to expected distributions (Page 12).

Probability Tables

Used to assess the likelihood of any distribution found in a class (Page 12).

Four Possible Outcomes

Determined by reality and what the researcher finds (Page 12).

Accurate Detection

Researcher's conclusion mirrors reality (Page 12).

Type I Error

Researcher concludes there is a relationship between two variables, but in reality, there is not (Page 13).

Type II Error

Data fail to show a relationship between variables that actually exists (Page 14).

Probability Values (p-values)

Used to set a threshold for type I or type II errors (Page 14).

Significance Level

"Significant at a p < .05 level" means that if the same study were repeated 100 times, this result would occur by chance fewer than five times (Page 14).

Replication

Scientific findings should be replicated in additional studies (Page 14).

Trustworthiness of Science

Scientific claims are more likely to be correct and predict real outcomes than "common sense" opinions and personal anecdotes (Page 14).

Scientific Theory

A comprehensive framework for making sense of evidence regarding a particular phenomenon (Page 14).

Difference from Everyday Usage

In common usage, a theory is an educated guess, while in science, it enjoys support from many research studies (Page 14-15).

Key Component

Good theories describe, explain, and predict in a way that can be empirically tested and potentially falsified (Page 15).

Open to Revision

Theories are open to revision if new evidence compels reexamination of the accumulated, relevant data (Page 15).

Example of Theory Revision

The shift from a geocentric to a heliocentric model of the solar system (Page 15).

Importance of Data

In science, we believe what the best and most data tell us, and we must be willing to change our views if better data come along (Page 16).

Thomas Kuhn's Perspective

Kuhn, a historian of science, posited that science is a social activity influenced by human psychology.

Challenge to Objectivity

He challenged the notion of purely objective theories and data, arguing that values inevitably inform all scientific endeavors.

Influence of Values

Scientists' personal and cultural values, experiences, and opinions shape the questions they ask and the interpretations they draw from their research.

Facts vs. Values

A crucial distinction exists between facts (objective information about the world) and values (beliefs about how the world is or should be).

Role of Values in Science

Contrary to the image of scientists as neutral observers, all science, especially social sciences like psychology, involves values and interpretation.

Levels of Analysis

A single phenomenon can be explained at different levels simultaneously.

Example of Levels of Analysis

Studying for a test (cramming vs. spaced practice) can be analyzed at low level (biochemical processes), mid level (cognitive processes), and high level (real-world behaviors).

Appropriateness of Analysis Levels

No single level of analysis is inherently "more correct"; appropriateness depends on the research question.

Understanding the World

Understanding the world requires considering multiple levels, from biochemistry to social behavior.

Multiple Ways of Interpreting the World

People use common sense, personal experience, and faith to navigate their culture.

Science as a Method of Understanding

Science provides another important way of understanding the world.

Strengths of Science

Systematic approach using testable, reliable data.

Strengths of Science

Ability to determine causality.

Strengths of Science

Capacity to generalize conclusions.

Limitations of Science

Subjectivity and uncertainty are inherent.

Limitations of Science

Understanding these limits does not render science useless.

Using Science as a Tool of Knowledge

Understanding how scientific conclusions are reached equips us to use science effectively.

Falsifiability

Falsifiability is a defining feature of science.

Inductive Reasoning

Drawing general conclusions from specific observations.

Example of Inductive Reasoning

Concluding the homeowner left in a hurry based on specific observations (stove on, door open).

Deductive Reasoning

Using a given premise to determine the interpretation of specific observations.

Example of Deductive Reasoning

Concluding the moon has weaker gravity than Earth because it has smaller mass, based on the general principle that gravity is associated with mass.

Example of Deductive Reasoning

Predicting students will not purchase textbooks based on the general principle that students do not like to pay for textbooks.

Example of Deductive Reasoning

Concluding Janine cannot graduate because she has fewer than the required 100 credits, based on the general principle that students need 100 credits to graduate.

Myth of Objectivity in Science

Challenges the traditional view of science as a purely objective endeavor.

Thomas Kuhn

Argued that science is a social activity influenced by human values and biases.

Science as a Social Activity

Science is not conducted in a vacuum; it's a human activity shaped by social and psychological factors.

Psychological Influences in Science

Kuhn argued that science is subject to the same psychological influences as any other human activity.

Objective Truths

The notion that science is a collection of objective truths discovered through neutral observation is challenged.

Influence of Values

Kuhn suggested that there is no such thing as objective theory or data.

Values in Science

All of science is informed by values, meaning that personal, cultural, and societal beliefs inevitably shape scientific inquiry.

Scientists' Values

Scientists' values influence the types of questions they ask and how they interpret their findings.

Facts vs. Values

Facts are objective information about the world, while values are beliefs about how the world is or ought to be.

Distinction between Facts and Values

While distinguishing between facts and values can be challenging, it's essential for understanding the subjective elements within scientific research.

Global Warming

The issue demonstrating the interplay between facts and values regarding human impact on greenhouse gas levels and weather patterns.

Facts

Accumulation of evidence substantiating the adverse impact of human activity on greenhouse gas levels and weather patterns.

Values

Beliefs that Earth is in danger and should be protected, influencing choices and behaviors aimed at addressing the impact.

Implications for Scientific Practice

Science functions best when people with diverse values and backgrounds work collectively to understand complex natural phenomena.

Multiple Perspectives

Enhance the robustness and reliability of scientific findings.

Levels of Analysis

A framework for understanding complex phenomena from multiple perspectives, where no single level is inherently superior.

Definition of Levels of Analysis

Refers to the idea that a single phenomenon can be explained at different levels simultaneously.

Cramming vs. Studying Over Time

The question can be addressed at various levels of analysis: Low Level (Biochemical), Mid Level (Cognitive), High Level (Behavioral).