Docsity
Docsity
Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity

Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan

Guidelines and tips
Guidelines and tips
Sell on Docsity
Sell on Docsity
Docsity AI
Docsity AI
Log inSign up

Prepare for your exams

Study with the several resources on Docsity

Find documents

Prepare for your exams with the study notes shared by other students like you on Docsity

Search for your university

Find the specific documents for your university's exams

Docsity AINEW

Summarize your documents, ask them questions, convert them into quizzes and concept maps

Explore questions

Clear up your doubts by reading the answers to questions asked by your fellow students

Earn points to download

Earn points by helping other students or get them with a premium plan

Share documents
download point icon20 Points

For each uploaded document

Answer questions
download point icon5 Points

For each given answer (max 1 per day)

All the ways to get free points
Get points immediately

Choose a premium plan with all the points you need

Study Opportunities

Choose your next study program

Get in touch with the best universities in the world. Search through thousands of universities and official partners

Community

Ask the community

Ask the community for help and clear up your study doubts

Free resources

Our save-the-student-ebooks!

Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors

Introduction to Psychological Statistics: Concepts and Applications, Exams of Statistics

A.T. Still University of Health Sciences (ATSU)Statistics

Psychological Statistics Psychological Statistics

Typology: Exams

2023/2024

Available from 04/01/2024

DrShirleyAurora
DrShirleyAurora 🇺🇸

4.4

(9)

6.2K documents

1 / 9

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
Psychological Statistics
Distribution -
- Any collection of scores, from either a sample or population
- Can be displayed in any form, but is usually represented as a histogram
Normal Distribution -
- Specific type of distribution that assumes a characteristic bell shape and is perfectly
symmetrical
What can distribution provide? -
- Can provide us with information on likelihood of obtaining a given score
Why is the Normal Distribution so important? -
- Almost all of the statistical tests that we will be covering (Z-Tests, T-Tests, ANOVA, etc.)
throughout the course assume that the population distribution, that our sample is drawn from (but for
the variable we are looking at), is normally distributed
- Also, many variables that psychologists and health professionals look at are normally distributed
- Why this is requires a detailed examination of the derivation of our statistics, that involves way more
detail than you need to use the statistic.
Z Score -
Represents the number of standard deviations a score is away from the mean
What are the scores that lie in the middle 50% of a distribution of scores with μ = 50 and σ = 10? -
Look for "Smaller Portion" = .2500 on table E.10
z = .67
Solve for X using z-score formula
Scores = 56.7 and 43.3
pf3
pf4
pf5
pf8
pf9

Partial preview of the text

Download Introduction to Psychological Statistics: Concepts and Applications and more Exams Statistics in PDF only on Docsity!

Psychological Statistics

Distribution -

  • Any collection of scores, from either a sample or population
  • Can be displayed in any form, but is usually represented as a histogram Normal Distribution -
  • Specific type of distribution that assumes a characteristic bell shape and is perfectly symmetrical What can distribution provide? -
  • Can provide us with information on likelihood of obtaining a given score Why is the Normal Distribution so important? -
  • Almost all of the statistical tests that we will be covering (Z-Tests, T-Tests, ANOVA, etc.) throughout the course assume that the population distribution, that our sample is drawn from (but for the variable we are looking at), is normally distributed
  • Also, many variables that psychologists and health professionals look at are normally distributed
  • Why this is requires a detailed examination of the derivation of our statistics, that involves way more detail than you need to use the statistic. Z Score - Represents the number of standard deviations a score is away from the mean What are the scores that lie in the middle 50% of a distribution of scores with μ = 50 and σ = 10? - Look for "Smaller Portion" = .2500 on table E. z =. Solve for X using z-score formula Scores = 56.7 and 43.

Standard Scores -

  • Scores with a predetermined mean and standard deviation, i.e. a z-score Why convert to a standard score? -
  • You can compare performance on two different tests with two different metrics
  • You can easily compute Percentile ranks but they are population-relative! Percentile - The point below which a certain percent of scores fall (Ex. If you are the 75th percentile, then 75% of the scores fall at or below your score) How do you compute percentile? -
  • Convert your raw score into a z-score
  • Look at table E.10, and find the "Smaller Portion" if your z-score is negative and the "Larger Portion" if it is positive
  • Multiply by 100 Basic Means to Describe a Set of Data (Descriptive Statistics) -
  • Measures of Central Tendency
  • Measures of Variability
  • Graphs
  • Z-Scores Sampling Error -
  • Variability of a statistic from sample to sample due to chance
  • Can potentially bias our results if it isn't equivalent across treatment and control groups

If, instead, we were testing if the group with anxiety was different from the average student population (Hint: Look at the italics), how would we phrase Ho and H1? - Ho = [x1 =/ x2] H1 = [x1 = x2] In our example of people with (x1) and without test anxiety (x2), where our hypothesis is that people with anxiety will have lower IQ scores: - Ho = [x1 ≥ x2] H1 = [x1 < x2] For our group with test anxiety, if their mean score on an IQ test was 70, we first convert this into a z- score (μ = 100, σ = 15) - z = (70 - 100)/15 = -

  • Since our H1 is that the group with anxiety will be less than those without, we look at the percent in the "Lesser Portion" Type I Error - α is the p("accepting" H1 when it is false/rejecting H0 when it is true), or of making a mistake Type II Error, or β (Beta) - p("accepting" H0 when it is false/rejecting H1 when it is true) Why not make α as small as possible? - Because as α [p(Type I Error)] decreases, β [p(Type II Error)] increases Reality/Hypothesis p>.05 p<. H1 (true) Type II Error :) H1 (false) :( Type I Error -
    • You never "prove" anything, only reject or fail to reject
  • We only draw conclusions related to the null hypothesis (H0) - so we only reject or fail to reject H0, not H It seems like we care more about Type I Error than Type II Error. Why? -
    • Scientists are more likely to commit a Type I Error because they are more motivated to prove their hypothesis (H1)
  • In Law, establishing motive is important to proving guilt, without a motive, there's little reason to expect that a crime will occur, let alone stringently attempt to protect against it One-Tailed/Directional Test - We can place all 5% in one "tail" of the distribution if we only expect a difference in means in one direction (Larger/smaller portion) Two-Tailed/Non-Directional Test - We can place half of 5% (2.5%) in either "tail", if we have no a priori (before) hypothesis about where our mean difference will be
  • The decision of which type of test to use should be made a priori based on theory, not data driven
  • Different For One Tailed Tests: - If our hypothesis is that group x is lower than group y Ho = (x ≥ y) H1 = (x < y) For Two-Tailed Tests: - If our hypothesis is that group x is either greater than or less than group y Ho = (x = y) H1 = (x ≠ y) Bonferroni Correction -
  1. If we have data that have been sampled from a population that is normally distributed with a mean of 50 and a standard deviation of 10, we would expect that 95% of our observations would lie in the interval that is approximately a. 30 - 70 b. 35 - 50 c. 45 - 55 d. 70 - 90 - a. 30 - 70
  2. Which of the following is not always true of a normal distribution? a. It is symmetric b. It has a mean of 0 c. It is unimodal d. All of the above are always true - b. It has a mean of 0
  3. If we were to repeat an experiment a large number of times and calculate a statistic such as the mean for each experiment, the distribution of these statistics would be called a. the distributional distribution b. the error distribution c. the sampling distribution d. the test outcome - c. the sampling distribution
  4. Sometimes we reject the null hypothesis when it is true. This is referred to as a. a Type I Error b. a Type II Error c. a mistake d. good fortune -

a. a Type I Error

  1. We would like to a. maximize the power of a test b. minimize the probability of a Type I Error c. do both (a) and (b) d. run tests that never make errors - c. do both (a) and (b)
  2. A two-tailed test is _________ powerful than a one-tailed test if the difference is in the direction that we would have predicted a. more b. less c. equally d. we cannot tell - b. less
  3. If the population from which we sample is normal, the sampling distribution of the mean a. will approach normal for large sample sizes b. will be slightly positively skewed c. will be normal d. will be normal only for small samples - a. will approach normal for large sample sizes
  4. What is the meaning of a 95% confidence interval? a. The two score that have a 5% probability of containing the sample mean b. The two scores that contain 95% of the distribution of sample scores c. The two scores that have a 95% probability of not containing the population mean d. The two scores that have a 95% probability of containing the population mean -