LECTURE 3 CHI SQUARED SIG TESTING Flashcards

1
Q

What are inferential statistics?

A

Statistical methods that allow us to make inferences about a population based on a sample.

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2
Q

What is the null hypothesis (H₀)?

A

A hypothesis stating there is no difference or association.

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3
Q

What is the alternative hypothesis (H₁)?

A

A hypothesis predicting a difference or association exists.

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4
Q

What is Null Hypothesis Significance Testing (NHST)?

A

A process estimating the probability of a result occurring by chance if the null hypothesis is true.

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5
Q

What is a p-value?

A

The probability that the observed result (or one more extreme) occurred by chance, assuming the null hypothesis is true.

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6
Q

What is the typical alpha level (α) for statistical significance?

A

0.05 or 5%.

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7
Q

What does p < 0.05 indicate?

A

The result is statistically significant, and we reject the null hypothesis.

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8
Q

What does p > 0.05 indicate?

A

The result is not statistically significant, and we fail to reject the null hypothesis.

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9
Q

What is a directional (one-tailed) hypothesis?

A

A hypothesis predicting the effect in a specific direction.

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10
Q

What is a non-directional (two-tailed) hypothesis?

A

A hypothesis predicting a difference or association without specifying the direction.

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11
Q

What are effect sizes?

A

Measures indicating the magnitude or strength of a difference or association.

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12
Q

What is Cohen’s d?

A

A popular effect size metric for comparing two groups.

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13
Q

What are the thresholds for Cohen’s d?

A

Small: 0.2, Medium: 0.5, Large: 0.8.

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14
Q

How do p-values differ from effect sizes?

A

P-values indicate significance; effect sizes indicate magnitude.

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15
Q

What is the purpose of Chi-square tests?

A

To examine relationships between two categorical variables.

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16
Q

What type of data is Chi-square used for?

A

Nominal or sometimes ordinal data.

17
Q

What are the assumptions of Chi-square tests?

A

Independent groups and expected frequencies greater than 5 in each cell.

18
Q

What is a 2x2 Chi-square?

A

A Chi-square test with two rows and two columns (e.g., binary variables).

19
Q

What is an observed frequency?

A

The actual count of cases in each category.

20
Q

What is an expected frequency?

A

The count we expect in each category if there is no association between variables.

21
Q

How do we calculate expected frequencies?

A

E= RowTotal×ColumnTotal / grand total

22
Q

What does a significant Chi-square result indicate?

A

An association between the variables.

23
Q

What is Cramer’s V?

A

An effect size measure for Chi-square tests.

24
Q

What are the thresholds for Cramer’s V?

A

Small: 0.1, Medium: 0.3, Large: 0.5 (for df = 1).

25
Q

What are the degrees of freedom (df) for Chi-square?

A

df=(r−1)(c−1), where r is rows and
c is columns.

26
Q

What is dummy coding?

A

Arbitrarily assigning numerical values to nominal data for analysis.

27
Q

What type of statistic is used for within-subjects ordinal data?

A

Wilcoxon Signed-Ranks test.

28
Q

What is the main difference between parametric and non-parametric tests?

A

Parametric tests assume normal distribution; non-parametric tests do not.

29
Q

What happens when assumptions of Chi-square are violated?

A

Results are less valid, requiring alternative analyses or adjustments.

30
Q

What is the role of significance testing?

A

To determine if the observed results are unlikely under the null hypothesis.

31
Q

Why are statistical tests like Chi-square important?

A

They provide a method to test hypotheses and draw conclusions about data.

32
Q

What does the p-value of 0.012 in the Zoella example indicate?

A

A 1.2% chance the results occurred by random chance, leading to rejection of the null hypothesis.

33
Q

What is the relationship between sample size and significance?

A

Larger samples reduce variability but may also make small effects significant.

34
Q

How are nominal level data presented?

A

As frequencies and percentages.

35
Q

Why are p-values controversial?

A

They are often misinterpreted as providing the probability of the hypothesis being true.