Psych 5 Final

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Trekofstarsx
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298286
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Psych 5 Final
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2015-03-15 15:57:25
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Psych
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Psych 5 Final Review
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  1. Hypothesis testing compares
    Known population before treatment and unknown population after treatment
  2. Hypothesis testing is usually used in what type of study
    research
  3. Hypothesis testing tests the ___ of the treatment
    effectiveness
  4. Hypothesis testing compares __ to eachother
    means

    • compare sample mean to population mean
    • or compare two or more sample means to eachother
  5. Differences between M and u are expected by __
    This is called __ __ and __ __
    • chance
    • sampling variability
    • sampling error
  6. Goal of hypothesis testing is to rule out __ chance as a plausible explanation for the results of a study
    chance
  7. Hypothesis Testing
    Statistical method using sample data to evaluate a hypothesis about a population
  8. Difference is statistically significant when 

    -
    -
    • -M is unlikely to have occurred by chance
    • -M is in the extreme tails of the sampling distribution
  9. "Unlikely" or "Extreme" percentage
    • -If M would occur 5% or less of the time
    • -p<0.05
  10. We always assume _ difference between the means, called the __ __
    no difference between the means, called the null hypothesis
  11. Null hypothesis
    -If true, M should be close to _
    -If M is not close to _, we reject
    • -u
    • -if M is not close to u, we reject the null hypothesis
  12. The type of statistics that deals with hypothesis testing
    inferential statistics
  13. Hypothesis Testing Steps

    -
    -
    -
    -
    • -State the hypothesis
    • -Set the criterion for a decision
    • -Compute the test statistic
    • -Make a decision
  14. Step 1:

    -
    -
    -Ho: Predicts there is no change, difference, or relationship between our treatment and the general population

    -H1: There is a change in the population after we apply the treatment
  15. H0 and H1 are __ __
    mutually exclusive (if one is true, the other cannot be)
  16. Non-directional hypothesis
    • -Also known as a two-tailed hypothesis
    • -Direction of difference is not specified
  17. Step 2:
    Create a decision rule, find the critical value, put probability, one or two-tailed
  18. Critical Value
    boundary between likely/unlikely outcomes
  19. Critical Region
    area under the curve more extreme than the critical value
  20. Decision Rule
    Reject H0 if observed test statistic falls in the critical region (exceeds critical value)
  21. Step 3:
    Compute the test statistic

    -z score formula
    -standard deviation formula
    See paper
  22. Step 4:
    Reject or fail to reject H0
  23. When we reject H0

    -Difference between means is unlikely to be due to __
    -We call this a __ __ finding
    -We can never __ or __ H0 or H1
    • -chance
    • -statistically significant
    • -prove or disprove
  24. When we fail to reject H0

    -Due to __
    -We call this __ __
    -We don't accept or prove H0, we just don't find evidence supporting _
    • -chance
    • -statistically significant 
    • -H1
  25. We never __ or __ a difference
    -prove or disprove
  26. Type 1 error
    • -Rejecting a null hypothesis when it's actually true
    • -Concluding a treatment has an effect when in reality it doesn't
  27. How do we control Type 1 Error?
    • -We do this with our alpha level, or p value
    • -a=0.05 --> there's a 5% chance we will falsely reject the null hypothesis
    • 5% chance of type 1 error
    • -a=0.01--> there's a 1% chance we will falsely reject the null hypothesis
  28. Type 2 Error
    • -Failing to reject a null hypothesis that is false
    • -The test failed to detect a real treatment effect
  29. B
    -probability of making a type II error when Ho is false
  30. Minimizing type 2 error is increasing __
    Based on __ and __
    • -power
    • -effect size and sample size
  31. Power

    -Power is greater when 

    1)
    2)
    • -Effect size is bigger because big effects are easy to use
    • -Sample size is larger because large samples make it easy to detect tiny effects
  32. We increase power (1-B) by

    1)
    2)
    3)
    4)
    • -Increase sample size (n) is the best possible way
    • -Increase treatment effect size
    • -Choose less stringent alpha level
    • -Use a one-tailed test
  33. Uncertainty and Error

    -We never know
    -We try to minimize
    -We balance the risk of
    -Three alpha levels researchers use
    • -The absolute truth
    • -p (making a mistake)
    • -Type I and Type II error
    • -0.05, 0.01 or 0.001
  34. Effect size

    Different types of statistics
    Descriptive statistic that indicates magnitude of an effect

    Cohen's d, r2, eta-squared
  35. Cohen's D formula

    What does Cohen's D tell us
    see paper

    -difference between means in standard deviation units
  36. Cohen's D

    -small effect
    -medium effect
    -large effect
    • -0.2
    • -0.5
    • -0.8
  37. Two-tailed tests (non-directional) have critical regions in
    One-tailed tests (directional) have critical region
    • -each tail
    • -in the lower negative tail only-for one tailed tests, critical value is smaller
  38. Assumptions of the z-test
    1)
    2)
    3)
    4)
    • Distribution of sample means is normal
    • Individuals were randomly sampled
    • Independent observations
    • Know that SD does not change with treatment
  39. What do you state when reporting results?

    1)
    2)
    3)
    4)
    • -Two means
    • -Z score
    • -probability
    • -tails
  40. Unimodal Distribution
    Distributions with one clear peak
  41. Bimodal DIstributions
    Distributions with two clear peaks
  42. Uniform Distribution
    • A probability distribution for which all of the values that a random variable can take on or occur with equal probability
    • Or the shape of a graph that has no peaks and valleys
  43. Symmetric Distribution
    Can be divided at the center so that each half is a mirror image of the other
  44. Standard Normal Distribution
    • Occurs when a normal random variable has a mean of zero and a standard deviation of one
    • The normal random variable is called a z score
  45. Skewed right
    Distributions with fewer observations on the right
  46. Skewed left
    Distributions with fewer observations on the left
  47. Normal Distribution
    • All normal distributions look like a symmetric, bell-shaped curve 
    • Associates the normal random variable X with a cumulative property
  48. Positive Skewness
    The tail on the right side is longer or fatter than the left side
  49. Negative Skewness
    Tail on the left side of the distribution is longer or fatter than the left side
  50. Long tail of a distribution
    Portion of the distribution having a large number of occurrences far from the "head" or central part of the distribution
  51. Body of a distribution
    Middle of the bell curve
  52. 1-B
    • -Probability of making a correct decision when His false
    • -Ability to detect a real treatment effect

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