Test Two

• Subjects


  The individuals on which the experiment is done are.
• Treatment


  A special and specific experimental condition applied to the subjects.
• Factor 


  In an experiment, they are explanatory variables.
• Double-Blind


  Neither the subjects nor the people who interact with them know which treatment each subject is receiving.
• Control/Comparison


  Control the effects of lurking variables on the response, by comparing two or more treatments (one of which can be a placebo). This avoids confounding (mixing up) the effects of a treatment with other variables.
• Randomization


  The use of impersonal chance to assign subjects to treatments at random. This can be done with a table of random digits.
• Double-Blind (If possible)


  If possible, make the experiment double-blind. In a double-blind experiment, neither the subjects nor the people who interact with them know which treatment each subject is receiving. It is not always possible to design an experiment to be double-blind.
• Bias


  In particular we are concerned about bias on the part of the person or people who evaluate the effectiveness of a treatment. To avoid this bias, we try to make the experiment double-blind.
• Lack of Realism


  The most serious potential weakness in the design of an experiment is lack of realism. This occurs when the subjects, treatments, and/or setting of an experiment do not realistically duplicate the conditions we really want to study.
• Random


  If individual outcomes are uncertain but there is a regular distribution of outcomes in a long series of repetitions.
• Probability 


  Any outcome of a random phenomenon is the proportion of times the outcome would occur in a long series of repetitions.
• Sample Space


  The set of all possible outcomes.
• Event


  Any (single) outcome or a set of outcomes of a random phenomenon.
• Probability Model


  A mathematical description of a random phenomenon consisting of two parts.
• Disjoint


  If they have no outcomes in common and so can never occur simultaneously.
• Random Variable


  A variable whose value is a numerical outcome of a random phenomenon.
• Probability Distribution 


  Tells us the values the variable takes and how to assign probabilities to the values (basically a probability distribution is a probability model).
• The probability of any event A (P(A)) satisfies 0≤P(A)≤1. It must be between 0 and 1.
• If P(A) = 0, then A never occurs.

  If P(A) = 1, then A always occurs.
• If S is the sample space in a probability model, then P(S) = 1.
• Addition Rule for Disjoint Events:

  Two events A and B are disjoint if they have no outcomes in common and so can never occur simultaneously.
• If A and B are disjoint events, then P(A or B) = P(A) + P(B).
• For any event A, P(A does not occur) = 1 – P(A). (Equivalent to: P(A) = 1 – P(A does not occur)).
• Finite Probability Model


  Assign a probability to each individual outcome in the sample space. These probabilities must be numbers between 0 and 1 (Rule 1) and the sum of the probabilities must be 1 (Rule 2). The probability of any event is the sum of the probabilities of the outcomes that make up the event. (Discrete models)
• Continuous Probability Model

  (You will need Table A when your model is a normal curve): A continuous probability model assigns probabilities as areas under a density curve. The probability of any event is the area under the density curve and the horizontal axis for the values that make up the event. Note: A normal curve is a continuous probability model since it is a density curve. (Infinite model)
• General Addition Rule for Any Two Events

  For any two events A and B, P(A or B) = P(A) + P(B) – P(A and B).
• Multiplication Rule for Independent Events

  Two events A and B are independent if knowing that one occurs does not change the probability that the other occurs.  (Recall from Chapter 8 that the digits in the Table of Random Digits (Table B) are independent of each other.) If A and B are independent events, then P(A and B) = P(A) x P(B).