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Reasoning in Psychology Using Statistics. Psychology 138 2017. Quiz 10, due Friday May 5 th . You may take it up to 10 times, your top score is what counts. Inferential Statistics : Procedures which allow us to make claims about the population based on sample data. Hypothesis testing
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Reasoning in PsychologyUsing Statistics Psychology 138 2017
Quiz 10, due Friday May 5th. You may take it up to 10 times, your top score is what counts.
Inferential Statistics: Procedures which allow us to make claims about the population based on sample data • Hypothesis testing • Correlation • Regression • Chi-squared test • Estimation • Point estimates • Confidence intervals • 1-sample z test • 1-sample t test • Related samples t-test • Independent samples t-test • Testing claims about populations (based on data collected from samples) • Using sample statistics to estimate the population parameters Lab Exam 4: Conclusions from Data
Analyze the question/problem. • The design of the research: how many groups, how many scores per person, is the population σ known, etc. • Write out what information is given • Is it asking you to test a difference, test a relationship, or make an estimate? • What is your critical value of your test statistic (z or t from table, you’ll need your α-level) • Now you are ready to do some computations • Write out all of the formulas that you’ll need • Then fill in the numbers as you know them • Interpret your final answer • Reject or fail to reject the null hypothesis? What does that mean? • State your confidence interval and what it means Performing your inferential statistics
The design determines the test Which test do I use?
Y SSY = 16.0 df = n - 2 SSX = 15.20 = 5 - 2 =3 SP = 14.0 rcrit = 0.878 6 5 H0: ρ =0 3 4 2 HA: ρ ≠ 0 1 X 1 2 3 4 5 6 Suppose that you notice that the more you study for an exam (X= hours of study), the better your exam score typically is (Y = exam score).Test if there is a significant correlation between the two variables (α = 0.05) A 6 6 B 1 2 C 5 6 D 3 4 E 3 2 Correlation 2-tailed Reject H0 There is a significant positive correlation between study time and exam performance Correlation within hypothesis testing
The design determines the test Which test do I use?
Y 6 5 4 3 2 1 1 2 3 4 5 6 X • The “best fitting line” is the one that minimizes the differences (error or residuals) between the predicted scores (the line) and the actual scores (the points) • Directly compute the equation for the best fitting line • Slope • Intercept • Also need a measure of error: • r2 (r-squared) • Sum of the squared residuals = SSresidual= SSerror • Standard error of estimate Regression
Y 6 5 3 4 2 Hypothesis testing on each of these 1 X 1 2 3 4 5 6 Suppose that you notice that the more you study for an exam (X= hours of study), the better your exam score typically is (Y = exam score).Compute the regression equation predicting exam score with study time. A 6 6 B 1 2 C 5 6 D 3 4 E 3 2 SSY = 16.0 Bi-variate regression SSX = 15.20 SP = 14.0 r2 = 0.806 Prediction with Bi-variate regression
Measures of Error • r2 • Standard error of the estimate • Unstandardized coefficients • “(Constant)” = intercept • Variable name = slope • These t-tests test hypotheses • H0: Intercept (constant) = 0 • H0: Slope = 0 • SPSS Regression output gives you a lot of stuff Hypothesis testing with Regression
The design determines the test Which test do I use?
When do we use these methods? • When we have categorical variables Step 1: State the hypotheses and select an alpha level Step 2: Compute your degrees of freedom df = (#Cols-1)*(#Rows-1) & Go to Chi-square statistic table and find the critical value Step 3: Obtain row and column totals and calculate the expected frequencies Step 4: compute the χ2 Step 5: Compare the computed statistic against the critical value and make a decision about your hypotheses Crosstabulation and χ2
Inferential Statistics: Procedures which allow us to make claims about the population based on sample data • Hypothesis testing • Correlation • Regression • Chi-squared test • Estimation • Point estimates • Confidence intervals • 1-sample z test • 1-sample t test • Related samples t-test • Independent samples t-test • Testing claims about populations (based on data collected from samples) • Using sample statistics to estimate the population parameters Lab Exam 4: Conclusions from Data
Age hours of studying per week hours of sleep per night pizza consumption • Describe the typical college student • Point estimates “12 hrs” • Interval estimates “2 to 21 hrs” “19 yrs” “17 to 21 yrs” “8 hrs” “1 per wk” “4 to 10 hrs” “0 to 8 per wk” Estimation of population parameters
Margin of error • Both kinds of estimates use the same basic procedure • Finding the right test statistic (z or t) • You begin by making a reasonable estimation of what the z (or t) value should be for your estimate. • For a point estimation, you want what? z (or t) = 0, right in the middle • For an interval, your values will depend on how confident you want to be in your estimate • Computing the point estimate orthe confidence interval: • Step 1: Take your “reasonable” estimate for your test statistic • Step 2: Put it into the formula • Step 3: Solve for the unknown population parameter Estimation
Sample size • As n decreases, the margin of error gets wider(changes the standard error) • Level of confidence • As confidence decreases (e.g., 95%-> 90%), the margin of error gets narrower (changes the critical test statistic values) • The size of the margin of error related to: Estimation
Make an interval estimate with 90% confidence of the population mean given a sample with a X = 85, n = 25, and a population σ = 5. Make an interval estimate with 95% confidence of the population mean given a sample with a X = 85, n = 25, and a population σ = 5. Make an interval estimate with 90% confidence of the population mean given a sample with a X = 85, n = 4, and a population σ = 5. All centered on 85 83 89 81 85 87 or 85 ± 1.96 86.96 83.04 narrower 86.65 83.35 or 85 ± 1.65 wider 89.13 80.88 or 85 ± 4.13 Estimates with z-scores
The design determines the formula that you’ll use for the estimation Which test do I use?
The design determines the formula that you’ll use for the estimation Design Estimation (Estimated) Standard error One sample, σ known One sample, σ unknown Two related samples, σ unknown Two independent samples, σ unknown Estimation Summary
Make an interval estimate with 95% confidence of the population mean given a sample with a X = 85, n = 25, and a population σ = 5. From the table: • z(1.96) =.0250 2.5% 2.5% 95% What two z-scores do 95% of the data lie between? So the 95% confidence interval is: 83.04 to 86.96 or 85 ± 1.96 Estimates with z-scores
Make an interval estimate with 95% confidence of the population mean given a sample with a X = 85, n = 25, and a sample s = 5. 2.5% 2.5% 95% What two critical t-scores do 95% of the data lie between? So the confidence interval is: 82.94 to 87.06 • From the table: • tcrit =+2.064 or 85 ± 2.064 Estimation in one sample t-design
Dr. S. Beach reported on the effectiveness of cognitive-behavioral therapy as a treatment for anorexia. He examined 12 patients, weighing each of them before and after the treatment. Estimate the average population weight gain for those undergoing the treatment with 90% confidence. Differences (post treatment - pre treatment weights): 10, 6, 3, 23, 18, 17, 0, 4, 21, 10, -2, 10 Related samples estimation Confidence level 90% CI(90%)= 5.72 to 14.28 Estimation in related samples design
Dr. Mnemonic develops a new treatment for patients with a memory disorder. He randomly assigns 8 patients to one of two samples. He then gives one sample (A) the new treatment but not the other (B) and then tests both groups with a memory test. Estimate the population difference between the two groups with 95% confidence. Independent samples t-test situation Confidence level 95% CI(95%)= -8.73to 19.73 Estimation in independent samples design