Module 3 - Statistics for Kinesiology
Nominal curve
The normal distribution is a probability distribution that is symmetric and bell-shaped
Kurtosis
A statistical measure used to describe the distribution of observed data points relative to a normal distribution, indicating the presence of outliers or the heaviness of the tails.
Bimodal & Multimodal curve
Distribution characteristics that include the presence of multiple peaks, indicating varying subpopulations within the data set.
Skewness
Statistical measures that describe the shape and features of data distributions.
Normal distribution and standard deviation
the mean or standard deviation between each section under the curve
What is a Z-score
Z-score is a raw score expressed in standard deviation units
How many SD an observation is away from a mean
If it is positive = above the mean
If it is negative = below the mean
Z-score and standardized normal distribution
The Z-score quantifies the number of standard deviations an observation is from the mean, providing a way to compare different data points within the same distribution.
Probability
The likelihood of an event occurring
Related to standard scores, percentiles and the characteristics of a normal distribution
There cannot be a negative probability
Probability density function and the normal curve
The probability density function is a function that describes the relative likelihood for a random variable to fall within a given range
Tests for normality – Lab
Histogram
Skewness & kurtosis
Kolmogorov-smirnov (K-S)
Shapiro-Wilk (S-W)
Null hypothesis = Data is normally distributed
Alternative hypothesis = data is not normally distributed
If calculated result > 0.05 (therefore not significant) we accept the null hypothesis
Outliers
Data point distinctly beyond the range of other data points observed
Can be identified
Visual inspection
Frequency tables
Histogram and bar graphs
Box-plots
Scatterplot matrices
It is real or an error?
Box-plots
useful for visualizing the distribution of data across different categories, highlighting medians, quartiles, and potential outliers.
Missing Data
2 options
Listwise deletion → drop that whole/subject from the analysis
Pairwise deletion → drop the variable from the analysis but use the others
Little’s missing completely at random (MCAR) test
Used to determine if missing values are random or no
This will dictate how to deal with the situation
Imputation: Replacing missing data
Multiple imputation
Maximum likelihood