bpk 304w Flashcards
What is the scientific method
- define problem
- develop a research question and hypothesis
3.design study and protocol - test hypothesis
compile results - communicate teh results
Making a good research question - key points
- specify the patients
- specify the intervention
- specify the control
- specify the outcomes
- WHO, WHAT, HOW, WHY
- Instead of asking it like a question - make it a statement that you will be trying to prove
eg - does it do this (wrong)
- This is what it does (right)
Validity vs reliability
Validity: systematic measurement errors that improves the accuracy of the result
Reliability: minimizing random measurement error that allows you to reproduce the results again
Testing the hypothesis for an effect or a difference (what test)
T-test, ANOVA, ANCOVA
testing the hypothesis for relationships or associations (what test)
correlation testing, regression, and p-value for significance
What is a Normal Frequency Distribution and what are the values at the peak
normal = 0 std (95% confidence)
1 std = 34% deviation
at the peak you find the:
Mean: the average value (use if normal dis)
Median: the absolute middle of the distribution (from least to greatest) (use if not normal dis)
Mode: the most often occurring value
Skewness
- what is it
- what does it represent
- what values are significant
Skewness is a measure of symmetry (or the lack of)
- this causes a bell curve to shape if it is perfectly symmetrical data
- the skewness can be negatively skewed (LEFT TAIL (less than -1)
- or positively skewed (RIGHT TAILmore than 1)
- if -1 < skewness< +1 = perfect normal
Kurtosis
- what is it
- what does it represent
- what values are significant
Kurtosis is a measure of peakedness
- high kurtosis (over 1) have more outliers and have more tail data than an even distribution
(distinct peak - leptokurtic)
- low kurtosis (between 1-3) have a more normal uniform distribution because they have less outliers and less tail data
(Flat peak - platykurtic)
normal = mesokurtic
Standard Deviation
to quantify the amount of variation, spread out numbers from the normal
Central Limit Theorem
the sampling mean will always be normally distributed if the population is large enough
Standard error of the mean (SEM)
how different the population mean would be in comparison to the sample mean (the accuracy of the results)
- this is obtained through your 95% confidence interval
(SD/sqrt(N)) –> as N inc the distribution will slowly become normal
- This is the error WITHIN the mean
Z scores (Standardization)
how far the mean is from a normal population - this is done by #std (unit) above or below the population mean
- this standardizes the data and ELIMINATES UNITS IN THE GRAPH – it’s just measured by STDs
- this does NOT change the distribution of the data
- done by making the reference Mean = 0 and the STD = 1 then seeing the scoring
internal norm of a Zscore
Standardizing based on changing the reference mean = 0 and STD = 1
- this is based on the mean and STD of the sample itself and *compares INTERNALLY - ind compared to other ind in that group
External Norm (Z-score)
Reference Mean and STD are changed and measured by NORMATIVE data’s mean and STD
- this compares the experiment group to the EXTERNAL normative data
Tscore
the same thing as a Z score but the reference mean and STD come from an external population (not normative data)
percentile
the percentage of population that lies at or below that score
- Eg. 95%, 75% 50% etc
inferential stats
you make relationships between variables and are able to test hypothesis
Testing for statistical significance
Compare the test statistic to the critical value (alpha - usually 0.05 @ 95% confidence interval)
Type 1 error (Alpha Error)
You conclude that the results are significant when they are actually not
- you reject the null hypothesis
- the larger your alpha is the more likely you are to make a type 1 error
p-value
probability that is NOT due to chance
- if your p-value is small the results obtained are likely not due to chance
- if your p-value is large is it likely that the result was because of chance (not real)
the alpha (significance level) is chosen as per the experiment but common is 0.05 (95% confidence)
type 2 error (Beta)
the probability of NOT rejecting the null hypothesis when your results are actually significant
one-tailed test
you are sure that the result only moves in ONE direction (either above or below normal distribution)
Two-tailed test
The alpha tests both directions of the study (above the normal distribution and below it)
Independent T-test Definition
&what’s the tvalue for this
Comparing 2 independent means from 2 different groups
- a T-stat is calculated depending on the difference of the means
* IT IS OPPOSITE FOR THE TVALUE*
– CALCULATED TVALUE HAS TO BE GREATER THAN THE CRITICAL TVALUE (0.05) TO REJECT NULL
*less than 120 subjects