soc data test 2

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62 Terms

1
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what is a normal curve?

bell shaped and symmetrical, mean, median, and mode are all at its peak

2
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is the normal curve based on theory or real data?

Theory; real data approximates it.

3
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what are the properties of normal distribution?

Symmetrical, bell-shaped, mean at peak, standard deviation changes the width

4
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Where is the mean in a normal curve?

at the peak

5
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What do standard deviations represent?

Distance from mean; larger SD = wider curve.

6
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What is a Z score?

Number of SDs a score is from the mean.

7
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Why is a Z score useful?

Finds probability, compares different distributions.

8
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What is sampling? Why do we do it?

Selecting a subset from a population to study; saves time and resources.

9
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what is a population

Entire group of interest.

10
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what is a sample

Subset of the population.

11
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what is a parameter

Value describing a population.

12
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what is a sample statistic

Value describing a sample.

13
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what is random sampling

Every member has equal chance of selection.

14
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what is systematic sampling

Selects every Nth member after a random start.

15
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what is stratified sampling

Population divided into subgroups, then sampled.

16
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what is proportionate stratified sample

Sample size matches subgroup proportion in population.

17
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what is sampling distribution

Theoretical distribution of all possible sample values.

18
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what is sampling error

Difference between sample statistic and population parameter.

19
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what is the central limit theorem

As sample size increases, the sampling distribution becomes normal. Larger sample size → smaller standard error (less variability).

20
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What is estimation?

Using a sample statistic to estimate a population parameter.

21
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What is the main goal of sampling theory and statistical inference?

To use sample data to make generalizations about a population.

22
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What is a point estimate?

A sample statistic that estimates the exact value of a population parameter.

23
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What is a confidence interval?

A range of values likely to contain the population parameter.

24
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What does a 95% confidence interval mean?

There is a 95% probability that the interval contains the population mean.

25
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What is a margin of error?

The range around a point estimate that accounts for sampling variability.

26
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What confidence level is commonly used?

95% is standard, but 90% or 99% may be used depending on precision needed.

27
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How can a researcher increase the precision of their estimate?

Increase sample size to reduce standard error.

28
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What is statistical hypothesis testing?

Evaluating hypotheses about population parameters using sample statistics.

29
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What are the assumptions of hypothesis testing?

Random sampling, interval-ratio level measurement, and normality (via CLT).

30
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What is the research hypothesis (H₁)?

A claim that the population parameter differs from a specified value.

31
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What is the null hypothesis (H₀)?

A statement of no difference, contradicting the research hypothesis.

32
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What is a one-tailed test?

Tests for an effect in one direction (left or right tail).

33
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What is a left-tailed test?

Tests if the sample outcome is in the left tail of the distribution.

34
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What is a right-tailed test?

Tests if the sample outcome is in the right tail of the distribution.

35
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What is a two-tailed test?

Tests for differences in both tails of the distribution.

36
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What is a Z statistic?

a sample statistic converted into a Z score.

37
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What is a p-value?

The probability of obtaining the test statistic under H₀.

38
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What is alpha (α)?

The probability threshold for rejecting H₀ (e.g., .05, .01, .001).

39
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What are the 5 steps of hypothesis testing?

  1. Make assumptions
    2. State hypotheses, select α
    3. Choose distribution, test statistic
    4. Compute test statistic
    5. Make a decision, interpret results

40
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What is a t statistic?

A test statistic used when the population standard deviation is unknown.

41
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When do we use a t statistic instead of a z statistic?

When the population standard deviation is unknown and estimated from the sample.

42
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What is the t distribution?

A family of curves based on degrees of freedom, used for small samples.

43
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What is a Type I error?

Rejecting a true null hypothesis (false positive).

44
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What is a Type II error?

Failing to reject a false null hypothesis (false negative).

45
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Why is the t test useful?

Compares groups, such as gender or racial income differences.

46
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What is bivariate analysis?

A method to detect and describe relationships between two variables.

47
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What is cross-tabulation?

A table-based technique to analyze relationships between two categorical variables.

48
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What is a bivariate table?

A table showing the distribution of one variable across categories of another.

49
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What is a column variable?

The independent variable, shown in the table’s columns.

50
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What is a row variable?

The dependent variable, shown in the table’s rows.

51
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What is a cell in a bivariate table?

The intersection of a row and a column, showing the count or percentage.

52
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What are marginals in a bivariate table?

The totals for rows and columns in a table.

53
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How do you measure the strength of a relationship?

By examining the percentage difference across the categories of the independent variable.

54
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What is a positive relationship?

When both variables increase or decrease together.

55
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What is a negative relationship?

When one variable increases while the other decreases.

56
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What is elaboration?

A process that introduces control variables to analyze a bivariate relationship.

57
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What are control variables?

Additional variables used to test or clarify a relationship.

58
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What are the three goals of elaboration?

1. Test for non-spuriousness
2. Clarify causal sequence
3. Identify conditions affecting the relationship

59
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What is a spurious relationship?

When both variables are influenced by a third variable, with no direct causal link.

60
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What is an intervening variable?

A variable that comes between the independent and dependent variable in a causal sequence.

61
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What is a conditional relationship?

When the effect of the independent variable on the dependent variable depends on a control variable.

62
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How do you test for non-spuriousness?

Introduce a control variable; if the original relationship remains after controlling for it, the relationship is non-spurious.