**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.

GOAL!

To Successfully interpret the following values:

Thank you!

Descriptive measures for Qualitative data

Frequency

Percentage

EXAMPLE

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

Quantitative data

CENTER

Mean

Median

Mode

CENTER

Mean:

Average of all values (Sum of values/Number of values).

Median:

The middle value of the group values.

Mode:

The most frequent value of group values.

Introduction to Bio-statistics

By:

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

Learning objectives

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.

SPREAD

SD

IQR

Range

SPREAD

Standard deviation (SD):

amount of variation away from mean.

Inter quartile range (IQR):

range of values between the 1st and 3rd quartile.

Range:

interval of values between minimum and maximum values.

Standard Deviation

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%].

IQR

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.

P value

CONFIDENCE INTERVAL

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).

EXAMPLE

ُْEXAMPLE

EXAMPLE

Diagnostic Statistics

Sensitivity

Specificity

False Positive

False Negative

Mean

Median

Mode

SD

IQR

Range

P value

CI

Sensitivity

Specificity

False +ve

False -ve