Final Exam Statistics.
The numerator of an inferential test statistic
-The actual difference between means
The denominator of an inferential test statistic
-the average distance between means -the distance between M and u
Standard error is determined by
1. Variability of raw scores (Standard Deviation) 2. Size of the sample (n)
Histogram or Polygraph
Interval and Ratio data
Why we use computational formua
It works with decimals
Inferential Statistics
Hypothesis testing, predicting from a sample to make a prediction on the population
Factor
Independent Variable
Distribution of F ratio is
positively skewed
Critical Region
Region of values that corresponds to the rejection of the null hypothesis
When is sampling error a problem?
When a sample is selected that doesn't represent the entire population
When is the median the most appropriate central tendency?
When the data is skewed
What does the sign of the z-score tell us?
Whether the value is above or below the mean
What values are possible for a F ratio?
Will always be positive
What does the absolute value of the z-score tell us?
How many standard deviations you are away from the mean
As sample size increases, standar error _______
Decreases
Degrees of Freedom
Describes how well the T statistic represents a Z score
Descriptive Statistics
Desribes, summarizes, organizes set of data, bar graph, mean, etc.
Nominal
Label and Categorize. Each group has no significance over the other EX. SSN
Variablility Definition
The spread of a distribution, whether clustered together or spread apart
Central Limit Theory
Used to specify the shape, central tendency and variability of the distribution of sample means
Single Sample Z Test
Used when comparing means, the known values are the population mean and SD
Single Sample T Test
Used when comparing means, the only known value is the population mean
Ratio
Absolute zero point. EX. Age, Height, Weight
Value of Correlations
Between -1.00 and 1.00
Quasi-Experiement vs Correlational
Both: Nothing is being manipulated Correlational: Same group of people, comparing two variables. Quasi: Comparing two separate groups
Related T Test
Comparing one group of people, 2 variables each
Independent T Test
Comparing two groups of people, one variable each
The T Statistic
Uses sample data to test hypotheses about an unknown population mean
When is it not necessary to use statistics to draw conclusions about a population?
Descriptive statistics uses the data to provide descriptions of the population, either through numerical calculations or graphs or tables. Inferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question.
Standard Deviation
Distance from a X raw score to a mean
Effect Size _____ change with sample size
Does NOT
Chi Square test
Examining relationships, Nominal data
Regression
Examining relationships, making a predicition based on knowledge from another variable
Hypothesis Testing
Goal is to rule out chance (sampling error) as a explanation for results
Sampling Distribution
How we estimate sampling error. It's a distribution, not a score
A larger smaple creates ______ discrpency (Sample Error)
Less
Which measure of central tendency can only be used for desriptive statistics?
Mode
Interval
Numerical Ordered categories, EX. Temperature
Bar Graph
Ordinal and Nominal Data
True experimental designs vs quasi experimental designs
Quasi- Experimental designs do not manipulate anything, and no random assignment
Three sources of Variance Discussed
Sum of Squares Variance Standard Deviation
What does the standar deviation measure?
Variability
Ordinal
categories with some order. Ranking. EX. 1st, 2nd, 3rd
Sampling Error
discrepancy between sample statistic and population parameter
Standard Error
distance from sample mean to population
Finding Probability
draw normal distribution, label mean and SD, identify and shade the area of interest. (Body, Tail, or Mean to Z)
Type II Error
false negative
Type I error
false positive
Experiment wise error
if we do multiple t tests, type I error accumulates
Purpose of Chi Square Test
intended to test how likely it is that an observed distribution is due to chance.
Variability of the sampling distribution
measured by standard error of mean
Probability
method for measuring the likelihood of obtaining a specific sample from a specific population