L4

• The z-score is a measure of how many standard deviations a data point is away from the mean, calculated by subtracting the mean from the data point and then dividing the result by the standard deviation

  
• The standard normal distribution is a bell-shaped curve with a mean of 0 and a standard deviation of 1, used to model various types of data

  
• The normal distribution is characterized by its mean (average value) and standard deviation (measure of spread), being symmetric and unimodal

  
• The table of the standard normal distribution shows the proportion of the distribution beyond a given z-score, aiding in understanding the distribution of data

  
• A graph displaying the normal distribution of oxygen consumption (VO2) in a population, with the curve being bell-shaped and the standard deviation indicated by the width of the curve

  
• The standard error of the mean (SE)

  
• The image shows different types of probability distributions: normal curve (symmetrical), positive skew curve (shifted left), and negative skew curve (shifted right)

  
• A graph representing a right-skewed distribution, where there are more extreme values on the right side than on the left side

  
• A graph displaying a left-skewed distribution, with the majority of data on the left side and a long tail extending to the right

  
• A kurtosis graph showing the "peakedness" or "flatness" of a distribution, with a normal distribution having a kurtosis of 0

  
• • Central Tendency in statistics includes mean, median, and mode
  • Dispersion measures the scattering of values from the mean, with variance and standard deviation being common measures
• • The Central Limit Theorem states that if random samples of size n are drawn from a normal population, the means of these samples will also form a normal distribution
  • As sample size increases, the variability of the sample means decreases
• The standard normal distribution table shows the proportion of the distribution beyond a given z-score, aiding in understanding the distribution of data points

  
• The image shows three types of probability distributions: normal curve (bell-shaped and symmetrical), positive skew curve (shifted to the left), and negative skew curve (shifted to the right)

  
• A right-skewed distribution graph represents a probability distribution with a longer tail on the right side, indicating more extreme values on that side

  
• A left-skewed distribution graph shows that the majority of data is on the left side, with a long tail extending to the right, positioning the mean, median, and mode to the right of the center

  
• The kurtosis graph measures the "peakedness" or "flatness" of a distribution, with a normal distribution having a kurtosis of 0, while a kurtosis greater than 0 is leptokurtic and less than 0 is platykurtic

  
• Central Tendency

  • mean
  • median
  • mode
• summarizing frequecy distribution

  1. central tendency
  2. dispersion
• Dispersion

  • Variance
  • σ
• parametric statistics

  • requires that the dependent variable be measurment data
  • data are independent
  • variances are equal (homoscedastic)
  • continous data
• AVOID Psudoreplication

  -artificially infaltes sample size
• skew

  measures assymetry of distrubution
• Kurtosis

  data that are bunched together