2.4—a statistical primer Flashcards

1
Q

2.4 Learning Objectives

A
  • know the key terminology of statistics.
  • understand how and why psychologists use significance tests.
    • significance tests are statistics that tell us whether differences between groups or distributions are meaningful.
    • how much variability there is among individuals within each of the groups will determine whether the averages are significantly different.
    • in some cases, the averages of the two groups may be different, yet not statistically different because the groups overlap so much.
  • apply your knowledge to interpret the most frequently used types of graphs.
  • analyze the choice of central tendency statistics based on the shape of the distribution.
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2
Q

2.4 Focus

A
  • how do psychologist use statistics to describe their observations?
  • how are statistics useful in testing the results of experiments?
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3
Q

Statistics

A
  • statistics can be boiled down to two general steps:
    • organize the numbers so that we can get a “big picture” view of the results.
      • this process is helped by the creation of tables or graphs.
    • test to see if any differences between groups or between experimental conditions are meaningful.
  • once these steps have been completed, it’s possible to determine whether the data supported or refuted the hypothesis.
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4
Q

Descriptive Statistics

A
  • descriptive statistics: a set of techniques used to organize, summarize, and interpret data.
  • this gives you the “big picture” of the results.
  • the statistics used to describe and understand the data are of three types: frequency, central tendency, and variability.
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5
Q

Distribution

A

made up of two pieces of information:

  • whether some numbers occurred more often than others,
  • and whether all of the numbers were clumped in the middle or more evenly spaced across the whole range.
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6
Q

Normal Distribution

A

(sometimes called the bell curve) a symmetrical distribution with values clustered around a central, mean value.

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7
Q

Negatively Skewed Distribution

A

a distribution in which the curve has an extended tail to the left of the cluster.

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8
Q

Positively Skewed Distribution

A

a distribution in which the long tail is on the right of the cluster.

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9
Q

Central Tendency

A

a measure of the central point of a distribution.

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10
Q

Mean

A

the arithmetic average of a set of numbers.

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11
Q

Median

A

the 50th percentile—the point on the horizontal axis at which 50% of all observations are lower, and 50% of all observations are higher.

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12
Q

Mode

A

the category with the highest frequency (i.e. the most observations).

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13
Q

Variability

A
  • variability: the degree to which scores are dispersed in a distribution.
  • high variability means that there are a larger number of cases that are closer to the extreme ends of the continuum for that set of data.
    • e.g. a lot of excellent students and a lot of poor students in a class.
  • low variability means that most of the scores are similar.
    • e.g. a class filled with B-students.
  • variability can be caused by:
    • measurement errors.
    • imperfect measurement tools.
    • differences between participants in the study.
    • and/or characteristics of participants on that given day (e.g. mood, fatigue levels).
  • if information about variability is not provided by the researcher, it is impossible to understand how well the measure of central tendency reflects the entire data set.
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14
Q

Standard Deviation

A

a measure of variability around the mean.

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15
Q

Hypothesis Test

A
  • hypothesis test: a statistical method of evaluating whether differences among groups are meaningful, or could have been arrived at by chance alone.
  • the difference in the central tendency for the two groups represents a “signal” that we are trying to detect.
  • the variability represents the “noise,” the outside forces that are making it difficult to detect the signal.
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16
Q

Statistical Significance

A

the means of the groups are farther apart than you would expect them to be by random chance alone.

17
Q

P-Value

A
  • p-value: the probability of the results being due to chance.
  • lower p-values indicate a decreased likelihood that your results were a fluke, and therefore an increased likelihood that you had a great idea and designed a good experiment.