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Introduction to bio-statistics
Transcript of Introduction to bio-statistics
Types of data
Qualitative data (Categorical),
in contrast, is for those aspects of your data where you make a distinction between different groups, and where you typically can list a small number of categories. This includes product type, gender, age group, etc.
To Successfully interpret the following values:
Descriptive measures for Qualitative data
The following values represents the BMI of women enrolled in an evaluation for a new treatment for obesity.
25/ 27/ 29/30/ 31/31/ 32/34/35 Kg/m
• Mean= =30.4 kg/m
• Median=31 kg/m
• Mode= 31 kg/m
• What if the BMI of the last woman increased?
Mean [will change], Median and Mode [No change]
This highlights the importance of the mean as it is more sensitive to changes on the periphery of data.
Descriptive measures for
Average of all values (Sum of values/Number of values).
The middle value of the group values.
The most frequent value of group values.
Introduction to Bio-statistics
Ahmed Said Negida
Third year student at Faculty of Medicine, Zagazig University
Head of scientific committee at Zagazig University- Student Research Unit
Co-founder & scientific coordinator at EMRA collaborative
Member at the American Academy of Neurology
National leader of GlobalSurg-1 research project
Advisor at Mendeley and Ambassador at Qx MD
1- Types of data
2- Interpretation of descriptive measures
3- Interpretation of inferential measures
4- Tips about: statistical tests
and statistics softwares.
Quantitative data (continuous)
is data where the values can change continuously, and you cannot count the number of different values.
Examples include weight, price, profits, counts, etc. Basically, anything you can measure or count is quantitative.
Standard deviation (SD):
amount of variation away from mean.
Inter quartile range (IQR):
range of values between the 1st and 3rd quartile.
interval of values between minimum and maximum values.
Interpret the following values ?!
* In a weight loss study. The weight of the population before gastric bypass surgery Mean (SD) = 130 (±15) kg
* In a prevalence study about depression among medical students. The age of students was Mean (SD) = 22 (±3) year
SD & statistical power
minimum, maximum and the three quartiles for a sample population. [1st quartile =25%,
2nd quartile= 50% =median, 3rd quartile =75%].
If we consider that statistical test usually test the hypothesis of the researcher (Alternative hypothesis) against the null (null hypothesis); then a significant p value (≤0.05) means that the null hypothesis is rejected. But if p value was >0.05, the null hypothesis could not be rejected and then the researcher's hypothesis has not been proved.
How to calculate P value?
Usually, You are given the P value (sig) with most statistical tests that you perform.
This term describes two values (the degree of confidence %) and (the interval of confidence). Confidence Interval (CI) means the interval (x1:x2) where you are confident (x%) that the true value lies within.
What is the true value?
An imaginary value that you will obtain if you include all the population within your study (did not take a sample). Because this true value is unknown -if known, you needn't do your research- we use an interval of high degree of confidence to express approaching the true value.
In a sample of 50 patients, we are testing a new sleeping pills for the treatment of insomnia. Number of sleeping hours were recorded all over one week before and after administrating the drug. The mean difference of sleeping hours was +2 hours,
95% CI (1.5: 2.5).
This means that we are 95% confident that if we recruit all patients with insomnia and treated them with this drug, the number of sleeping hours of all population will increase by a number of hours ranging from (1.5 hours to 2.5 hours).