Scientific Experiment Fundamentals

Chapter 1: Introduction to Statistics

• Statistics is a branch of mathematics focused on collecting, organizing, analyzing, interpreting, and presenting data.
• It includes procedures for analyzing data and is used to represent sample data.
• Statistics are crucial for making sense of data in fields like psychology.
• Data are typically numeric values (plural) or scores, measurements, or observations (datum is singular).

Outline of Chapter 1

• Definition of Statistics
• Descriptive vs. Inferential Statistics
• Basics of Experiments
• Scales of Measurement
• Classification of Variables
• Order of Operations

Descriptive vs. Inferential Statistics

• Descriptive statistics summarize and organize data.
  • Include percentages (e.g., 64% voter preference); measures of central tendency (e.g., avg human height); and measures of dispersion (e.g., range of test scores).
• Inferential statistics make conclusions about a population based on sample data.
  • Examples include t-tests, F-tests, correlation, regression and chi-square tests.
  • Used to answer research questions about traits related to physical activity, verbal skills differences, or heritability of schizophrenia.

Populations and Samples

• A population encompasses all individuals, items, or data of interest in a study (e.g., all people with a gambling addiction).
• A sample is a subset of the population studied (e.g., 30 people with gambling addiction).
• Samples are often used because studying an entire population is often impractical.

Parameters and Statistics

• A parameter describes a characteristic of a population (e.g., the average SAT score for all test takers).
• A statistic describes a characteristic of a sample (e.g., the average SAT score for a sample of 30 students).

Random Sampling

• Random sampling is essential for accurately drawing conclusions about a population from sample data.
• Each member of the population has an equal chance of being selected.
• Random assignment is a way to randomly select participants for groups in an experiment.

Sampling Procedures

• Methods other than random sampling can be used when truly random sampling is not feasible (e.g., using existing records, locations, or offering incentives).

The Basics of Experiments

• Experiments involve manipulating variables to study cause-and-effect relationships.
• Subjects (Ss)/ participants are the individuals included in an experiment.
• Examples of research questions include assessing whether sugar consumption causes hyperactivity in children.
• A scientific experiment uses a random sample of children to test for causation.

Variables (IV, DV, and Extraneous)

• Variables are factors that vary, and include characteristics such as height, weight, eye color, etc.
• Independent variable (IV) is the variable manipulated in a study (e.g., sugar in the sugar experiment).
• Dependent variable (DV) is the variable measured in a study (e.g., level of hyperactivity in the sugar experiment).
• Extraneous variables are other variables that could affect the DV (e.g., food eaten prior to the experiment).

Experimental and Control Conditions

• Experimental condition: the group that receives the independent variable (treatment).
• Control condition: the group that does not receive the independent variable (non-treatment).

Random Assignment in Experiments

• Random assignment ensures that any difference in the dependent variable between groups is due to the independent variable, not other factors that may affect the outcome.
• It reduces the risk of bias in experiments.

Scales of Measurement

• Nominal scale: categorizes data without implying order (e.g., gender, eye color).
• Ordinal scale: categorizes data with order (e.g., rank in a race).
• Interval scale: categorizes data with order and equal intervals (e.g., temperature).
• Ratio scale: categorizes data with order, equal intervals, and a true zero point (e.g., weight, height).

Classification of Variables

• Continuous variables: can take on any value within a given range (e.g., height).
• Discrete variables: can take on only specific values (e.g., number of siblings).
• Quantitative variables: represent amounts or quantities (e.g., height, weight).
• Qualitative variables: represent categories or qualities (e.g., gender, eye colour).

Order of Operations

• Statistics employ calculations following established order, including parentheses, exponentiation, and sum operations.

Chapter 2: Frequency Distributions

• Frequency distributions provide a summary of how often different scores or ranges of scores occur in data.

• Useful for efficiently communicating important data like the most frequent scores.

• Provide a variety of visual aids such as pictogram, histograms, frequency polygons, ogives, bar graphs and pie charts.

• A stem-and-leaf display shows the distribution of data, organizing scores for easy interpretation.

• Bar charts visually represent frequencies of discrete data.

• Pie charts show relative proportions of categorical data.