Logic Exam 6

Front 1

In statistics, a generic quality is called a

Back 1

Variable

Front 2

In statistics, specific qualities are called

Back 2

values of a variable

Front 3

A statement that gives the actual number of Ss that are P

Back 3

An absolute frequency

Front 4

A statement that gives the proportion of Ss that are P

Back 4

A relative frequency

Front 5

The X that lies in the middle of all the Xs, so that the equal number of Xs are higher and lower.

Back 5

The median

Front 6

Sum of the values of each S and then divide by the number of Ss

Back 6

Arithmetic mean

Front 7

The total number of things in a class S or the sum of their values on a variable.

Back 7

Total

Front 8

The number or sum of Ss per unit of some other class T

Back 8

Ratio

Front 9

The number or proportion of Ss that have some property P

Back 9

Frequency, F=1/F

Front 10

The proportion of Ss that have each of the values (P, Q, R, etc.) on some variable, qualitative or quantitative.

Back 10

Distribution

Front 11

The central value (either the mean or the median) of the Ss on some quantitative variable.

Back 11

Average

Front 12

General method of statisticians

Back 12

Ransom sampling: the reason is that if we choose our sample randomly, then every member of the population has an equal chance of being included in the sample.

Front 13

Important random sampling implications

Back 13

1. There is always a specific margin of error attached to the conclusion we draw about the population.
2. the margin of error depends on the size of the sample. The larger the sample, the smaller the margin of error, and vice versa.
3. We should be aware that even when we take the margin of error into account, we are still only dealing with probabilities.

Front 14

When statistical methods are used to support a causal generalization that factor A affects E, the chief questions to ask about the internal validity of the inference are:

Back 14

1. Confounding variables: Were there any variables, other than the ones being tested, that may have been responsible for E?
2. Direction of causality: Is it clear whether A is affecting E or E affecting A?

Front 15

The chief questions to ask about external validity are:

Back 15

1. Extrapolation: is the claim that A affects E being extended beyond the class of A's or Es, or extrapolated beyond the range of the variables that were tested?
2. Proxy variables: When a causal factor or an effect cannot be studied directly, and other variables are studied as their proxies, how reasonable is it to assume a connection between the observed variable and the variation of interest?

Front 16

That which is to be explained

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Explanandum

Front 17

Rules of adequacy

Back 17

1. The inference from hypothesis to explanandum should have a high degree of logical strength.
2. The explanation should be complete. It should explain all significant aspects of the explanandum.
3. The explanation should be informative: The hypothesis should state the fundamental cause or reason for the explanadum.

Front 18

One hypothesis is more plausible than another if it is

Back 18

More consistent with the rest of our knowledge and if it is simpler.

Front 19

Testing an hypothesis indirectly

Back 19

1. Derive consequences from the hypothesis and then see whether the consequences are true.
2. If one or more of the consequences are false, reject the hypothesis unless it would be simpler or more consistent with other knowledge to reject an auxiliary hypothesis.
3. If a number of consequences are true, the hypothesis is equally consistent wit the same evidence and is simple.