Chapter 5: Sampling and Probability Flashcards
Goal of most researchers
To collect data from a sample that represents the population
Two main types of samples
Random samples and Convenient samples
Random Sample
One in which every member of the population has an equal chance of being selected into the study
Convenience Sample
One that uses participants who are readily available
Generalizability
Can also be referred to as External validity; Refers to researchers’ ability to apply findings from one sample or in one context to other samples or contexts
Replication
Refers to the duplication of scientific results, ideally in a different context or with a sample that has different characteristics
Volunteer Sample
A special kind of convenience sample in which participants actively choose to participate in a study
Problems with a Biased Sample
One person can never constitute a representative sample; it wouldn’t even make sense to calculate statistics on data from one person. This is a special kind of convenience sample a volunteer sample
Random Assignment
Every participant has an equal chance of being assigned to any level of the independent variable;
Random Selection
Almost never used; refers to a method of creating a sample from a population
Random Assignment
Refers to a method we can use once we have a sample, whether or not the sample is randomly selected; more frequently used
Confirmation Bias
Unintentional tendency to pay attention to evidence that confirms what we already believe and to ignore evidence that would disconfirm our beliefs
Illusory Correlation
The phenomenon of believing one sees an association between variables when no such association exists
Personal Probability
The likelihood of an even occurring based on an individual’s opinion or judgment; also called subjective probability; can also be called a guesstimate
Probability
In statistics is the likelihood that a particular outcome will occur out of all possible outcomes
Expected Relative-Frequency Probability
The likelihood of an event occurring based on the actual outcome of many, many trials
Relative
Indicates that this number is relative to the overall number of trials
Expected
Indicates that it’s what we would anticipate, which might be different from what actually occurs
Trial
Refers to each occasion that a given procedure is carried out
Outcome
Refers to the result of a trial
Success
Refers to the outcome for which we are trying to determine the probability
Formula for probability
probability = successes/trials
Steps to calculate probability
- Determine the total number of trials
- Determine the number of these trials that are considered successful outcomes
- Divide the number of successful outcomes by the number of trials
The Law of Large Numbers
That probability only works only in the long run; in the long run, results are predictable
Key factor in statistical probability
The individual trials must be independent
Independent
In statistics, it means that the outcome of each trial must not depend in anyway on the outcome of previous trials
Inferential Statistics
Hypothesis testing; helps to determine how likely a given outcome is
Control Group
A level of the independent variable that does not receive the treatment of interest in a study
Experimental Group
A level of the independent variable that receives the treatment or intervention of interest
Hypothesis Compared in Inferential Statistics
Null Hypothesis and Research Hypothesis
Null Hypothesis
A statement that postulates that there is no difference between populations or that the difference is in a direction opposite from that anticipated by the researcher; It proposes that nothing will happen
Research Hypothesis
Alternative hypothesis; the exciting one; a statement that postulates that thee is a difference between populations or sometimes, more specifically, that there is a difference in a certain direction, positive or negative; proposes a distinctive difference that is worthy of further investigation
Making a decision about the hypothesis
Decide to reject the null hypothesis
Decide to fail to reject the null hypothesis
Two things that can be done after data is analyzed
Reject the null hypothesis; “I reject the idea that there is no mean difference between populations;”
Fail to reject the null hypothesis. “I do not reject the idea that there is no mean difference between populations.”
Three rules of formal hypothesis testing
- The null hypothesis is that there is no difference between groups, and usually our hypotheses explore the possibility of a mean difference
- We either reject or fail to reject the null hypothesis. There are no other options
- We never use the word accept in reference to formal hypothesis
Two types of error using statistical language
Type I Errors and Type II Errors
Type I Error
Occurs when we reject the null hypothesis, but the null hypothesis is correct; similar to a false-positive in a medical test
Type II Error
Occurs when we fail to reject the null hypothesis, but the null hypothesis is false; like a false-negative in medical testing; indicates that we falsely failed to reject the null hypothesis; results in a failure to take action because a research intervention is not supported or in medical testing, a given diagnosis is not received.
