Measures of Central Tendency 

• Central tendency (or statistical averages) tell us the point about which items have a tendency to cluster.  
• Mean, Median and mode are the most popular averages.  
• Such a measure is considered as the most representative figure for the entire mass of data.  

Mean, also known as arithmetic average, is the most common measure of central tendency. 

Median is the value of the middle item of series when it is arranged in ascending or descending order of magnitude. It divides the series into two halves. 

Mode is the most commonly or frequently occurring value in a series.  

Like median, mode is a positional average and is not affected by the values of extreme items. 

Measures of Dispersion 

(i) Range

(ii) Mean deviation

(iii) Standard deviation

(iv) Co-efficient of variation

Range 

• Range is the simplest measure of dispersion.  
• It is the difference between the highest and lowest.  
• This is not a satisfactory measure as it is based only on two extreme values.  
• It does not consider all the observations to find range.   

Mean deviation 

• Mean deviation is found by summing up the differences from the mean and divide by the number of observation.  
• Though simple, easy to calculate and better measure of variation, mean deviation is not used in statistical analysis as it cannot be used in further mathematical expressions.  

Standard deviation 

• It is defined as square root of mean squared deviations. 
• Commonly used along with mean  

Co-efficient of variation 

• It is a measure used to compare relative variability, i.e. to compare the variability between two characteristics or group.  
• Shows the extent of variability of data in a sample in relation to the mean of the population. 
• Co-efficient of variation is expressed in terms of percentage. 
• Co-efficient of Variation (CV) = (SD/Mean) × 100  

Standard Error  

• Standard deviation of the means of sample is called standard error.  

Hypothesis  

• A statistical hypothesis is an assumption about a population parameter.
• Null hypothesis (H0) which assumes no effect in the population.
• Alternative hypothesis (H1) which holds if the null hypothesis is not true. 

Type I and Type II errors 

• A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population.;  it is true and there is no difference between the two groups. 
• A type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population; that is, it is false and there is a difference between the two groups which is the alternative hypothesis. 

Power of the study

• Refers to the number of patients required to avoid a type II error in a comparative study. 

Article by Dr. Siri P. B.