Week 3 Flashcards
Statistics
Descriptive data from a sample
Parameters
A samples corresponding values in the wider populations
Sampling Error
The random variability in a statistic from sample to sample
2 ways that a statistical relationship in a sample can be measured
- There is a relationship in the population and the relationship in the sample reflects this
- There is no relationship in the population and the relationship in the sample only represents sampling error
Null hypothesis testing
Formal approach to deciding between interpretations of a sample
Null Hypothesis
- No relationship in the population and the relationship in the sample is sampling error
- Chance relationship
Alternative Hypothesis
- There is a relationship in the population and the relationship in the sample reflects this
Reject the Ho
-A decision made by researchers using null hypothesis testing which occurs when the sample relationship would be extremely unlikely
- Researches always assume that the Ho is true
Retain the Ho
A decision made by researchers in null hypothesis testing which occurs when the sample relationship would not be extremely unlikely
P Value
The probability of the sample result/ a more extreme result if the null hypothesis were true
High P value
Sample/ more extreme result would be likely if the Ho were true and leads to the retention of the Ho
Low P Value
Sample/ more extreme result would be unlikely if the Ho were true and leads to the retention of the Ho
Alpha
-The criterion that shows how low a p value should be before the sample result is considered unlikely enough to reject the ho
- Usually set to 0.05
Statistically Significant
- An effect that is unlikely due to random chance and therefore likely represents a real effect in the population
- 5% chance or less; Ho rejected
- More than 5% chance; Ho is retained
Role of sample size and relationship strength
- The stronger the relationship, the lower the p value
- The larger the sample size, the lower the p value
Practical/ Clinical significance
- The importance/ usefulness of a result in the real world context
Descriptive Statistics
Describes a typical response/ behaviour of a group as a whole
Central Tendency
Indicators of the type of data that we can expect from the group as a whole
Measures of central tendency
- Mean
-Mode
-Median
Mean
-Add all values and divide by number of values
- May not be viable if you have an outlier
Mode
- most common
Median
Middle value when all the values are arranged in numerical order
Dispersion
The variability or consistency in a set of data
Measures of Dispersion
Range
Variance
Standard Deviation