Ds vs Is discuss 3.1
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This document discusses descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, and summary statistics like the mean, median, mode, variance, and standard deviation. Inferential statistics allow generalization from samples to populations through hypothesis testing, where the null hypothesis is tested against the alternative hypothesis. Type I and type II errors are possible, and significance tests control the probability of type I errors through the alpha level while power analysis aims to reduce type II errors. Common inferential tests mentioned include t-tests, ANOVA, and meta-analysis.
Ds vs Is discuss 3.1
- 1. Lecture 5: Chapter 5 Statistical Analysis of Data …yes the “S” word
- 2. What is a Statistic???? Population Sample Sample Sample Sample Parameter: value that describes a population Statistic: a value that describes a sample PSYCH always using samples!!!
- 3. Descriptive & Inferential Statistics Descriptive Statistics • Organize • Summarize • Simplify • Presentation of data Inferential Statistics • Generalize from samples to pops • Hypothesis testing • Relationships among variables Describing data Make predictions
- 4. Descriptive Statistics 3 Types 1. Frequency Distributions 3. Summary Stats 2. Graphical Representations # of Ss that fall in a particular category Describe data in just one number Graphs & Tables
- 5. 1. Frequency Distributions # of Ss that fall in a particular category How many males and how many females are in our class? Frequency (%) ? ? ?/tot x 100 ?/tot x 100 -----% ------% total scale of measurement? nominal
- 6. 1. Frequency Distributions # of Ss that fall in a particular category Categorize on the basis of more that one variable at same time CROSS-TABULATION Democrats Republican total 24 1 25 19 6 25 Total 43 7 50
- 7. 1. Frequency Distributions How many brothers & sisters do you have? # of bros & sis Frequency 7 ? 6 ? 5 ? 4 ? 3 ? 2 ? 1 ? 0 ?
- 8. 2. Graphical Representations Graphs & Tables Bar graph (ratio data - quantitative)
- 9. Histogram of the categorical variables 2. Graphical Representations
- 10. Polygon - Line Graph 2. Graphical Representations
- 11. 2. Graphical Representations Graphs & Tables How many brothers & sisters do you have? Lets plot class data: HISTOGRAM # of bros & sis Frequency 7 ? 6 ? 5 ? 4 ? 3 ? 2 ? 1 ? 0 ?
- 12. Altman, D. G et al. BMJ 1995;310:298 Central Limit Theorem: the larger the sample size, the closer a distribution will approximate the normal distribution or A distribution of scores taken at random from any distribution will tend to form a normal curve jagged smooth
- 13. 2.5% 2.5% 5% region of rejection of null hypothesis Non directional Two Tail body temperature, shoe sizes, diameters of trees, Wt, height etc… IQ 68% 95% 13.5% 13.5% Normal Distribution: half the scores above mean…half below (symmetrical)
- 15. Summary Statistics describe data in just 2 numbers Measures of central tendency • typical average score Measures of variability • typical average variation
- 16. Measures of Central Tendency • Quantitative data: – Mode – the most frequently occurring observation – Median – the middle value in the data (50 50 ) – Mean – arithmetic average • Qualitative data: – Mode – always appropriate – Mean – never appropriate
- 17. Mean • The most common and most useful average • Mean = sum of all observations number of all observations • Observations can be added in any order. • Sample vs population • Sample mean = X • Population mean =m • Summation sign = • Sample size = n • Population size = N Notation
- 18. Special Property of the Mean Balance Point • The sum of all observations expressed as positive and negative deviations from the mean always equals zero!!!! – The mean is the single point of equilibrium (balance) in a data set • The mean is affected by all values in the data set – If you change a single value, the mean changes.
- 19. The mean is the single point of equilibrium (balance) in a data set SEE FOR YOURSELF!!! Lets do the Math
- 20. Summary Statistics describe data in just 2 numbers Measures of central tendency • typical average score Measures of variability • typical average variation 1. range: distance from the lowest to the highest (use 2 data points) 2. Variance: (use all data points) 3. Standard Deviation 4. Standard Error of the Mean
- 21. Descriptive & Inferential Statistics Descriptive Statistics • Organize • Summarize • Simplify • Presentation of data Inferential Statistics • Generalize from samples to pops • Hypothesis testing • Relationships among variables Describing data Make predictions
- 22. Measures of Variability 2. Variance: (use all data points): average of the distance that each score is from the mean (Squared deviation from the mean) Notation for variance s2 3. Standard Deviation= SD= s2 4. Standard Error of the mean = SEM = SD/ n
- 23. Inferential Statistics Population Sample Draw inferences about the larger group Sample Sample Sample
- 24. Sampling Error: variability among samples due to chance vs population Or true differences? Are just due to sampling error? Probability….. Error…misleading…not a mistake
- 25. Probability • Numerical indication of how likely it is that a given event will occur (General Definition)“hum…what’s the probability it will rain?” • Statistical probability: the odds that what we observed in the sample did not occur because of error (random and/or systematic)“hum…what’s the probability that my results are not just due to chance” • In other words, the probability associated with a statistic is the level of confidence we have that the sample group that we measured actually represents the total population
- 26. data Are our inferences valid?…Best we can do is to calculate probability about inferences
- 27. Inferential Statistics: uses sample data to evaluate the credibility of a hypothesis about a population NULL Hypothesis: NULL (nullus - latin): “not any” no differences between means H0 : m1 = m2 “H- Naught”Always testing the null hypothesis
- 28. Inferential statistics: uses sample data to evaluate the credibility of a hypothesis about a population Hypothesis: Scientific or alternative hypothesis Predicts that there are differences between the groups H1 : m1 = m2
- 29. Hypothesis A statement about what findings are expected null hypothesis "the two groups will not differ“ alternative hypothesis "group A will do better than group B" "group A and B will not perform the same"
- 30. Inferential Statistics When making comparisons btw 2 sample means there are 2 possibilities Null hypothesis is true Null hypothesis is false Not reject the Null Hypothesis Reject the Null hypothesis
- 31. Possible Outcomes in Hypothesis Testing (Decision) Null is True Null is False Accept Reject Correct Decision Correct Decision Error Error Type I Error Type II Error Type I Error: Rejecting a True Hypothesis Type II Error: Accepting a False Hypothesis
- 32. Hypothesis Testing - Decision Decision Right or Wrong? But we can know the probability of being right or wrong Can specify and control the probability of making TYPE I of TYPE II Error Try to keep it small…
- 33. ALPHA the probability of making a type I error depends on the criterion you use to accept or reject the null hypothesis = significance level (smaller you make alpha, the less likely you are to commit error) 0.05 (5 chances in 100 that the difference observed was really due to sampling error – 5% of the time a type I error will occur) Possible Outcomes in Hypothesis Testing Null is True Null is False Accept Reject Correct Decision Correct Decision Error Error Type I Error Type II Error Alpha (a) Difference observed is really just sampling error The prob. of type one error
- 34. When we do statistical analysis… if alpha (p value- significance level) greater than 0.05 WE ACCEPT THE NULL HYPOTHESIS is equal to or less that 0.05 we REJECT THE NULL (difference btw means)
- 35. 2.5% 2.5% 5% region of rejection of null hypothesis Non directional Two Tail
- 36. 5% 5% region of rejection of null hypothesis Directional One Tail
- 37. BETA Probability of making type II error occurs when we fail to reject the Null when we should have Possible Outcomes in Hypothesis Testing Null is True Null is False Accept Reject Correct Decision Correct Decision Error Error Type I Error Type II Error Beta (b) Difference observed is real Failed to reject the Null POWER: ability to reduce type II error
- 38. POWER: ability to reduce type II error (1-Beta) – Power Analysis The power to find an effect if an effect is present 1. Increase our n 2. Decrease variability 3. More precise measurements Effect Size: measure of the size of the difference between means attributed to the treatment
- 39. Inferential statistics Significance testing: Practical vs statistical significance
- 40. Inferential statistics Used for Testing for Mean Differences T-test: when experiments include only 2 groups a. Independent b. Correlated i. Within-subjects ii. Matched Based on the t statistic (critical values) based on df & alpha level
- 41. Inferential statistics Used for Testing for Mean Differences Analysis of Variance (ANOVA): used when comparing more than 2 groups 1. Between Subjects 2. Within Subjects – repeated measures Based on the f statistic (critical values) based on df & alpha level More than one IV = factorial (iv=factors) Only one IV=one-way anova
- 42. Inferential statistics Meta-Analysis: Allows for statistical averaging of results From independent studies of the same phenomenon