Introduction to Psychological Statistics

• What Statistics are NOT

  • Statistics is not mathematics
  • Statistics is a way of viewing reality as exists around us in a way that we otherwise could not
• Goals of Statistics

  • Collecting data
  • Classifying data
  • Summarizing
  • Analyzing data
  • Presenting data
  • Interpreting data
• Statistics


  • The science of data. This involves collecting, classifying, summarizing, organizing, analyzing, presenting, and interpreting numerical information.
  • Include numerical facts and figures.
• Psychology


  • The scientific study of mind and behavior. Psychologists are actively involved in studying and understanding mental processes, brain functions, and behavior.
  • Describe, Explain, Predict, and Control.
• Fundamental Elements of Statistics

  • Population
  • Parameter
  • Sample
  • Statistics
  • Variable
  • Values
  • Score
  • Data
  • Data set
  • Sampling error
  • Statistical inference
• Quantitative data


  Measurements that are recorded on a naturally occurring numerical scale
• Qualitative data


  Measurements that cannot be measured on a natural numerical scale; they can only be classified into one of a group of categories
• Application of Statistics

  • Descriptive statistics
  • Inferential statistics
• Types of Variables

  • Discrete Variable
  • Continuous Variable
• Scales of Measurement

  • Nominal Scale - categorical data and numbers that are simply used as identifiers or names represent a nominal scale of measurement.
  • Ordinal Scale - represents anordered series of relationships or rank order.
  • Interval Scale - represents quantity and has equal units but for which zero simply represents an additional point of measurement is an interval scale.
  • Ratio Scale - similar to the interval scale in that it also represents quantity and has equality of units.
• Importance of learning statistics

  • Understanding statistics is crucial to being able to read psychological research articles.
  • It is also crucial to doing research yourself.
  • Develops your analytic and critical thinking.
• Types of data

  • Primary data - refers to the firsthand data gathered by the researcher himself.
  • Secondary data refers to the data collected by someone else earlier.
• Classifying data


  Depending on the sensitivity of the data an organization holds, there needs to be data classification levels to determine elements including who has access to that data and how long the data needs to be retained.
• Classification of data

  1. Public data - freely accessible to the public.
  2. Internal data - strictly accessible to internal company personnel or internal employees who are granted access.
  3. Confidential data - requires specific authorization and/or clearance.
  4. Restricted data - any restricted data stored or handled by government or other institutions that have authorization or authentication requirements associated with its use.
• Summarizing and Analyzing data

  Tallying data or sorting data by its classification.

  Includes mean, median, and mode.
• Analyzing data


  utilizes statistical tools such as T-test, Analysis of variance test (ANOVA).
• Presenting data


  comparing two or more data sets with visual aids.
• Types of Presenting data:

  1. Textual form - use words, sentences and paragraphs.
  2. Tabular form - a systematic presentation of data in rows and columns.
  3. Graphical form - shows numerical values or relationships in pictural forms such as graphs, symbols, etc.
• Interpreting data


  the process of reviewing data and arriving at relevant conclusions and recommendations.
• Population


  the set of all individuals of interest in a particular study.
• Parameter


  a value, usually a numerical value that describes a population. A parameter is usually derived from measurements of the individuals in the population.
• Sample


  a set of individuals selected from a population, usually intended to represent the population in a research study.
• Statistics


  a value, usually a numerical value that describes a sample. A statistic is usually derived from measurements of the individuals in the sample.
• Variable


  a characteristics or condition that changes or has different values for different individuals.
• Values


  the possible number or category a value can have.

  Ex: 1.00-5.00, Male/Female
• Score


  or raw score, a particular sample’s value on a variable.
• Data


  are measurements or observations. A datum (singular) is a single measurement or observation and is commonly called a score or raw score.
• Two types of data

  1. quantitative data
  2. qualitative data
• Data set


  a collection of measurements or observations.
• Sampling error


  the naturally occurring discrepancy, or error, that exists between a sample statistic and the corresponding population parameter.
• Statistical inference


  an estimate, prediction, or some other generalizations about a population based on information contained in a sample.
• Descriptive statistics


  statistical procedures used to summarize, organize, and simplify data.

  utilizes numerical and graphical methods to look for patterns in a data set, to summarize the information revealed in a data set, and to present that information in a convenient form.
• Inferential statistics


  consist of techniques that allow us to study samples and then make generalizations about the populations from which they were selected.

  utilizes sample data to make estimates, decisions, predictions or other generalizations about a larger set of data.
• Discrete variables


  consist of separate, indivisible categories. No values can exist between two neighboring categories
• Continuous variables


  there are an infinite number of possible values that fall between any observed values. A continuous variable is divisible into an infinite number of fractional parts