# HSCI 307 Lecture Notes - Lecture 11: Alternative Hypothesis, Standard Error, Statistical Hypothesis Testing

59 views1 pages Types of data
Data: numbers that represent some feature of the thing you are measuring
Measurement: the process of assigning numbers to the thing
Categories NOIR
Nominal: mutually exclusive & exhaustive
Ordinal: nominal + in order
Interval: nominal + in order + equally spaced
Ratio: nominal + in order + equally spaced + a
meaningful zero
Discrete or Continuous
Discrete: who number,
no value between the
whole numbers
Pareto charts
Stem and leaf plots (SLP)
Box plots
Scatter plots
Graph data with some common tools that allow you to see data patterns
Each one
Describe data numerically
Correlation coefficient: straight line relationship between two variables
Descriptive statistics: happen to describe properties of the data and their distribution
One group of data
Minimum ~ maximum ~ range - a measure of variation
Median: two equally frequent parts
Decimal place (dp) 0.1
Mean:
Standard deviation: how much each observation varies from the mean
Three number report: sample size, median, IQR (interquartile range)
Lifetimes of people and electronic devices (computer)
Skewed left or has a tail that is longer on the left than it is on the right
Length of stay of patients in a hospital
Skewed right or has a longer tail on the right than on the left
Measure of symmetry - skewness (-1 ~ 1)
Five-number summary
Two groups of data
Correlation
The regression equation
Confidence intervals (CI)
Lower bound and upper bound - two-sided confidence interval
Parameter: a characteristic of a distribution of a variable in a
population that was used to generate the sample
Ideally - sample should be selected at random/ created as
part of a RCT - to ensure the groups represent the
population (otherwise bias)
data are independent sample from the population
Normally distributed
RIN for random, independent, normally distributed
CI is a statement about a characteristic in a population that was
used to generate the sample - Statistical inference, rather than a
descriptive statistic
95% - 95% confidence coefficient (19 times out of 20)
95% CI: 95% of the time the true value in the population will be
contained in the interval if the study were to be repeated many times
Assumptions
Standard error (SE)
Criterion value
Approximate CI
Difference between the means
Equal variances
Test hypothesis
Step 1: compute a test statistic
Step 2: compute a P-value from a suitable referent distribution
Step 3: compare the P-value to a criterion standard - α(0.05 - 95%)
P-value < α, hypothesis is rejected - statistically significant
P-value > α, hypothesis cannot be rejected
Null hypothesis, one expects to be rejected
Alternative hypothesis
Two-tailed test
Step 4: interpretation:
Hypothesis testing: another way to draw an inference about the population from which the sample of
data was collected
Quantitative methods: analysis
Monday, March 20, 2017
20:18
week 11 chpt 11 quantitative analysis Page 1
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## Document Summary

Data: numbers that represent some feature of the thing you are measuring. Measurement: the process of assigning numbers to the thing. Graph data with some common tools that allow you to see data patterns. Interval: nominal + in order + equally spaced. Ratio: nominal + in order + equally spaced + a meaningful zero. Discrete: who number, no value between the whole numbers. 95% ci: 95% of the time the true value in the population will be contained in the interval if the study were to be repeated many times. Descriptive statistics: happen to describe properties of the data and their distribution. Correlation coefficient: straight line relationship between two variables. Minimum ~ maximum ~ range - a measure of variation. Standard deviation: how much each observation varies from the mean. Three number report: sample size, median, iqr (interquartile range) Measure of symmetry - skewness (-1 ~ 1)