Questions and Answers
What is a placebo treatment?
What does 'single blind' mean in an experiment?
When either the participant or the experimenter is unaware of whether the participant is getting the real treatment or a placebo treatment.
What is the definition of a double blind study?
Neither the participant nor the research assistant knows what type of treatment the participant is receiving.
What is an experimental group?
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What is a control group?
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What is an empty control group?
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What is an independent variable?
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What are levels of an independent variable?
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What is the dependent variable?
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What does independent random assignment mean?
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What is the purpose of experimental hypothesis?
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What is the null hypothesis?
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What does internal validity mean?
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What are inferential statistics?
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What is the mean?
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What is the Central Limit Theorem?
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What is a T-Test?
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What indicates statistical significance?
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What does p <.05 indicate?
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What is a Type 1 error?
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What is a Type 2 error?
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What is power in research?
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What are null results?
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Study Notes
Placebo Treatment
- Refers to a fake treatment with no therapeutic effect, used to measure the power of suggestion.
- Commonly utilized in medical trials where participants may receive a non-active pill.
Single Blind
- Only one party, either the participant or the experimenter, is unaware of the treatment assignment.
- Helps reduce bias in results by keeping participants or experimenters blind to treatments.
Double Blind
- Both the participant and the research assistant do not know which treatment is being administered.
- Minimizes both participant and experimenter bias, enhancing study integrity.
Experimental Group
- Comprises participants randomly assigned to receive the treatment being tested.
Control Group
- Consists of participants who do not receive the treatment, serving as a comparison to the experimental group.
Empty Control Group
- This group receives no treatment whatsoever, not even a placebo.
- Can lead to ambiguity in results as it doesn't account for potential placebo effects.
Problem with Empty Control Group
- If the treatment group performs better, it’s unclear if this is due to the actual treatment or a placebo effect.
- Researchers often avoid empty control groups to enhance construct validity.
Independent Variable
- The manipulated variable within an experiment, which the experimental group receives more of compared to the control group.
Levels of Independent Variable
- Different amounts or variations of the treatment variable are provided to participants.
Dependent Variable
- The outcome that the researcher measures, reflecting the effects of the independent variable.
Dependent Variable in Simple Experiment
- The dependent variable is hypothesized to change as a result of the independent variable's influence.
Independently
- A crucial assumption in statistical testing, where the outcome of one participant must not influence another.
Assigning Participants
- Individual assignment to treatment or control conditions, along with independent testing, ensures independence among participants.
Independent Random Assignment
- Randomly decides participant treatment allocation without regard to previous assignments, crucial for maintaining experimental rigor.
Experimental Hypothesis
- A prediction asserting that the treatment will yield an effect, linking the independent variable to the dependent variable.
Null Hypothesis
- States there is no treatment effect, implying observed differences are coincidental rather than caused by the treatment.
Null Hypothesis: Proving
- While it can be disproven, it cannot be definitively proven; disproving lends credence to the experimental hypothesis.
Simple Experiment
- Characterized by randomly assigning participants to one of two groups, enhancing the ability to establish causation.
Internal Validity
- Refers to a study's capacity to accurately ascertain if the independent variable causes an effect, exclusive to experimental designs.
Inferential Statistics
- The methodology for making inferences about a population based on sample data.
Population
- Represents the entire group of interest, which can be analyzed through large random samples.
Mean
- An average computed by summing all scores and dividing by the number of scores.
Central Limit Theorem
- Indicates that larger sample sizes lead to normally distributed sample means, critical for statistical testing assumptions.
T-Test
- A primary analysis method for examining data from simple experiments, assessing group mean differences.
T-Test: Computing Ratio
- Involves calculating the ratio between differences in group means and the standard error of these differences.
T-Test: Statistical Significance
- Results are statistically significant if the observed difference surpasses three times the standard error; varies with participant count.
T-Test: Absolute Value
- If the calculated T value exceeds the standard tabled value, the results are considered significant.
Statistical Significance
- Indicates results are likely not due to chance, supporting the hypothesis.
p < 0.05
- Suggests that if no effect existed, observed differences would occur less than 5% of the time due to chance alone.
Type 1 Error
- Occurs when the null hypothesis is incorrectly rejected, leading to false discoveries; less than a 5% chance if p < 0.05.
Type 2 Error
- Results from failing to reject the null hypothesis when it should be; implies missing an actual treatment effect.
Power
- The likelihood of detecting a true difference, hence preventing type 2 errors.
Null Results
- Outcomes that do not disprove the null hypothesis but do not confirm it either; often tied to issues of power and potential type 2 errors.
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