Hypothesis Testing Flashcards

1
Q

Core Steps of Hypothesis Testing

A
  1. State your hypotheses:
    H₀: the default assumption (e.g., "the average 
    price = 3500")
    
    H₁: the alternative (e.g., "the average price ≠ 
      3500")
  2. Choose a test depending on:
    Type of data (numeric/categorical)
    
    Number of variables (1 or 2)
    
     Sample size
  3. Run the test in SPSS
  4. Check the p-valueIf p < 0.05, reject H₀ (you have strong evidence)If p > 0.05, fail to reject H₀ (not enough evidence)
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2
Q

One-sample T-test

A

Compare a sample mean to a known value

Eg. Is the average diamond price > $3500?

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2
Q

Paired-samples T-test

A

Compare two variables for the same group

Is the average length of diamonds different from their width?

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3
Q

Independent-samples T-test

A

Compare means between two groups

Eg. Are lab-grown diamonds heavier than natural ones?

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4
Q

Chi-Square Test

A

Check association between two categories

Is diamond color associated with type (natural/lab-grown)?

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5
Q

Two-sided (≠)

A

You just want to check if it’s different (higher OR lower).

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6
Q

One-sided (> or <)

A

You want to check in a specific direction (just higher or just lower).

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7
Q

What does a p-value tell you?

A

The probability that your results happened by chance. If p < 0.05, we reject the null hypothesis.

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8
Q

What are H₀ and H₁ in hypothesis testing?

A

H₀ is the null hypothesis (no effect), H₁ is the alternative hypothesis (there is an effect).

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9
Q

When do you use a one-sample T-test?

A

When comparing a sample mean to a known value (e.g., is average price = $3500?).

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10
Q

When do you use an independent samples T-test?

A

To compare the means of two different groups (e.g., lab-grown vs natural diamonds).

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11
Q

What is the difference between a one-sided and two-sided test?

A

A one-sided test looks for a difference in one direction (greater or less), while a two-sided test checks for any difference (≠).

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12
Q

What type of test should you use to check if two categorical variables are related?

A

Chi-Square Test.

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13
Q

What does it mean if your p-value is 0.03?

A

There’s only a 3% chance the result is due to randomness. Reject H₀ with 95% confidence.

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14
Q

What kind of error is it if you reject H₀ when it’s actually true?

A

Type I Error (false positive).

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15
Q

What kind of error is it if you fail to reject H₀ when it’s false?

A

Type II Error (false negative).

16
Q

Kolmogorov–Smirnov Test

A

Check if a variable follows a distribution

17
Q

What test is used to compare the means across more than two groups?

A

One-Way ANOVA (Analysis of Variance)

18
Q

What is the null hypothesis in an ANOVA test?

A

All group means are equal.

19
Q

What does a significant result in ANOVA tell you?

A

At least one group is different, but not which one—you need further post-hoc tests to know more.

20
Q

What is the SPSS path for running a One-Way ANOVA?

A

Analyze → Compare Means → One-Way ANOVA

21
Q

When should you use correlation analysis?

A

When examining the relationship between two numeric (interval/ratio) variables.

22
Q

What does the correlation coefficient (r) indicate?

A

The strength and direction of a linear relationship:

r ≈ 1 → Strong positive

r ≈ -1 → Strong negative

r ≈ 0 → Weak/No correlation

23
Q

What SPSS path do you follow to run a correlation analysis?

A

Analyze → Correlate → Bivariate

24
How do you interpret a correlation result with r = 0.264 and p = 0.036?
Weak positive correlation, statistically significant at 5% level.
25
What is the key difference between an Independent Samples T-Test and ANOVA?
T-Test compares 2 groups, while ANOVA compares more than 2 groups.
26
What type of test do you use to check for differences between more than 2 group means?
One-Way ANOVA
27
What test compares the means of two numeric variables within the same group?
Paired-Samples T-Test
28
When would you use a one-sided test instead of a two-sided test?
When you only care if the mean is greater than or less than a value (not just different).
29
What are the four types of measurement scales?
Nominal: Labels (e.g., gender) Ordinal: Rank order (e.g., satisfaction level) Interval: Numeric, no true zero (e.g., temperature) Ratio: Numeric, true zero (e.g., weight, income)