Notes on Session: Scientific Methods, Experiments, and Variables

Icebreaker and Setup

Two-minute self-assessment: students note down what they know about the topics and what they still need to learn.
PDF was shared in the chat; participants can download it and follow along.
Preference expressed to not change pages mid-session; continue with the current document.

Question posed: What are we trying to think about when we discuss scientific methods?
Emphasis on identifying the steps of the scientific method.
The speaker notes that the first step in a method is the start of the scientific method, highlighting the importance of establishing the initial step.
Concept: there are multiple steps in the method, with the first step being foundational.

In discussing experiments, the concept of variables is introduced.
A question is raised: what is the difference between the two key variables?
The terms mentioned: "independent and dependent variables?" and the idea of manipulation.
The instructor notes that in experiments, there is a response that is observed or collected, i.e., the dependent variable (the data).
The process involves manipulating one variable and observing the resulting change in another.

Independent Variable (IV): the variable that the experimenter deliberately changes or manipulates to observe its effect.
Dependent Variable (DV): the variable that is observed and measured in response to changes in the IV.
Relationship concept: the DV is a function of the IV, often summarized as DV depends on IV.
Common example (from the discussion context):
- IV = amount of a factor you change (e.g., fertilizer, hours of sunlight, etc.).
- DV = plant height or another measurable response.
Expressed form: the core idea can be written as DV = f(IV), representing the dependence of the outcome on the manipulated input.

Scenario concept: changing the IV (e.g., amount of fertilizer) and measuring the DV (e.g., plant height).
A concrete illustration (for study planning):
- IV: amount of fertilizer (grams) – values could be (0, 5, 10, 15) grams.
- DV: plant height after a fixed period (cm).
Purpose: to observe how changes in the IV influence the DV, enabling inference about potential causal relationships.

The IV/DV framework supports causal inference by linking manipulated inputs to observed outcomes.
Distinguishing IV from DV helps prevent conflating cause and effect and guides proper experimental design.
In real-world contexts, controlling for confounding factors (not explicitly mentioned in the transcript but relevant) strengthens conclusions drawn from the experiment.

Scientific method: typically includes forming a question, hypothesizing, designing an experiment, collecting data, analyzing results, and drawing conclusions.
Independent Variable (IV): IV = \text{manipulated value (the value the experimenter changes)}
Dependent Variable (DV): DV = \text{observed outcome (the response measured)}
Relationship: DV = f(IV)
Control variables: variables kept constant to isolate the effect of the IV (not deeply covered in this excerpt but commonly part of experimental design).
Notation for a simple linear expectation: DV = a \cdot IV + b where (a) is the slope and (b) is the intercept (illustrative of how DV might respond to changes in IV).

Start with an icebreaker to gauge topic confidence.
Share and follow a PDF document to guide the session.
Introduce scientific methods and emphasize identifying the initial step.
Discuss experiments and the core distinction between independent and dependent variables.
Highlight how manipulating the IV leads to observed changes in the DV (the collected data).

This foundation sets up deeper exploration of experimental design, validity, reliability, and data analysis in subsequent topics.
Expect to encounter more detailed examples, controls, and statistical methods in later lectures.