Week 4: Probability and Chance Flashcards
What is probability?
Chance an event will occur
0% chance to 100% chance
What is Normal distribution?
When the mean, median and mode are equal
Symmetrical
Technically only theoretical, but often seen in the real world/nature
Connecting normal distribution to standard
deviation
Total probability = 100%
If we have a normal distribution, we can estimate the probability of finding a value
Standard deviation vs standard error
Standard deviation tells us how spread out or different individual observations are in a group (sample)
Standard error helps us understand how close the average of a small group of data (sample) is to the true average of a
larger group (population)
A smaller standard error suggests that the sample mean is likely close to the true population mean, while a larger
standard error indicates greater variability and less precision in estimating the population mean
What is confidence interval?
The confidence interval is an estimate of the amount of uncertainty associated with a sample, computed from the statistics of the observed data. The interval has an associated confidence level that, loosely speaking, quantifies the level of confidence that the parameter lies in the interval. More strictly speaking, the confidence level represents the frequency (i.e. the proportion) of possible confidence intervals that contain the true value of the unknown population parameter.
How does confidence interval affect how sure you are about your results?
Wide CI: less sure, tight CI: more sure
Hypothesis testing
Inferential research - we make
inferences about a population using a sample
Idea that can be tested in the real world
States a relationship between variables
Negative or “null” findings can also be very important!
Null Hypothesis
No association H0, It is a statement of no effect or no difference.
You never “accept” the null hypothesis
You “fail to reject” the null hypothesis:
You haven’t “proved” the null- there’s just not enough evidence to reject
Example: In the judicial system you can never “prove” someone is innocent you can just fail to prove they’re guilty
Alternative Hypothesis
There is an association or effect
You also don’t “accept” the alternative
You “reject” the null hypothesis: enough evidence that it is very unlikely that the null is true
All possible alternatives to the null hypothesis
What % chance are you willing to accept that you “randomly” found an association
Implementation of a Multidisciplinary Team
Approach Improves Pain Management for
Post-Surgical Patients
Null Hypothesis (H0): There is no significant difference in pain management outcomes for post-surgical patients between
facilities that employ a multidisciplinary team approach and those that do not.
Alternative Hypothesis (HA): Facilities that employ a multidisciplinary team approach demonstrate improved pain
management outcomes for post-surgical patients compared to facilities that do not use such an approach.
The Use of Mindfulness-Based Stress Reduction (MBSR) Techniques Reduces Stress Levels and Enhances Coping Skills in Nursing Students
Null Hypothesis (H0): There is no significant difference in stress levels or coping skills between nursing students who
undergo MBSR training and those who do not.
Alternative Hypothesis (HA): Nursing students who undergo MBSR training exhibit a significant reduction in stress levels
and an improvement in coping skills compared to those who do not receive such training.
Can we think of another example of a null and alternative hypothesis?
Probability sample
Probability of selection is known- you need access to entire population
(may not be feasible for large population)
Simple random sample: random selection entire population
Systematic: randomly select every 5th person
Stratified: divide on characteristic
Cluster: randomly select smaller groups
Example: our class as a population
Another population example?
Non-probability sample
probability of selection is not known
Convenience: who’s available
Quota: similar to stratified but non random
What is a Type I error?
A) Rejecting a true null hypothesis
