Measures of Central Tendency: Mean Medium Mode
Measures Of Central Tendency: Mean Medium Mode
Mode instead of mean… Categorical variables, words not numbers
Measures of Dispersion: Standard Deviation, Range, and Variables
Range = Largest number minus smallest number
SD = Average Distance from the Mean (Most frequently used)
Variance:
Fat & Skinny Distributions: Skewness – measure of the lack of symmetry, or the lopsidedness of a distribution. One “tail” of the distribution is longer than another.
Kurtosis: has to do with how flat or peaked a distribution appears/
Platykurtic: flat
Leptokurtic: peake
Hypotheses
Null Hypothesis: There is NO RELATIONSHIP between our IV and DV
Non-Directional Hypothesis:
Directional Hypothesis: We believe we know the direction
What makes a good hypothesis? Testable Can’t be in a question Tell a relationship between Stated in a declarative form Brief and to the point
Reflects theory or past literature
Tells a relationship between variables
The Normal Curve The Bell Shaped Curve Mean=Median=Mode
Symmetrical
Asymmetric Curve
Statistical Inference
The Central Limits Theorem: When samples are large (above 30) the sampling distribution will take the shape of a normal distribution regardless of the shape of the population
Ultimate Goal Accepting or Rejecting the NULL hypothesis
Accept or Reject? We accept a null hypothesis when the significance level greater than .05 Reject when less than .05
Confidence Intervals First you have to know three things:
Statistical inference: the process of using sample statistics to estimate population parameters.
Confidence level: the probability that a population parameter lies within a given confidence interval
Confidence interval: the range of values in which the population parameter is estimated to fall.
A statistic is an estimate of a parameter But the statistic will rarely equal the parameter exactly. Because of sample error
If our mean
Mode instead of mean… Categorical variables, words not numbers
Measures of Dispersion: Standard Deviation, Range, and Variables
Range = Largest number minus smallest number
SD = Average Distance from the Mean (Most frequently used)
Variance:
Fat & Skinny Distributions: Skewness – measure of the lack of symmetry, or the lopsidedness of a distribution. One “tail” of the distribution is longer than another.
Kurtosis: has to do with how flat or peaked a distribution appears/
Platykurtic: flat
Leptokurtic: peake
Hypotheses
Null Hypothesis: There is NO RELATIONSHIP between our IV and DV
Non-Directional Hypothesis:
Directional Hypothesis: We believe we know the direction
What makes a good hypothesis? Testable Can’t be in a question Tell a relationship between Stated in a declarative form Brief and to the point
Reflects theory or past literature
Tells a relationship between variables
The Normal Curve The Bell Shaped Curve Mean=Median=Mode
Symmetrical
Asymmetric Curve
Statistical Inference
The Central Limits Theorem: When samples are large (above 30) the sampling distribution will take the shape of a normal distribution regardless of the shape of the population
Ultimate Goal Accepting or Rejecting the NULL hypothesis
Accept or Reject? We accept a null hypothesis when the significance level greater than .05 Reject when less than .05
Confidence Intervals First you have to know three things:
Statistical inference: the process of using sample statistics to estimate population parameters.
Confidence level: the probability that a population parameter lies within a given confidence interval
Confidence interval: the range of values in which the population parameter is estimated to fall.
A statistic is an estimate of a parameter But the statistic will rarely equal the parameter exactly. Because of sample error
If our mean