is a value, usually a numerical value, that describes a population. A paramet may be obtained from a single measurement, or it may be derived from set of measurements from a population  

example is a population average

a single meaurement orobservation

also known as raw score or score

Data is pural

Experimental method

Experimental research strategy

one specific research method that involves comparing groups of scores

are boundaries of intervals for scores that are represented on a continuous number line. The real limit separating 2 adjacent scores is located exactly halfway between the scores

Each score has 2 real limits: Upper and lower real limits

summation

sum of the scores

is a statistical measure to determine a single score that defines the center of a distribution. The goal of central tendency is to find the single score that is most typical or most representative of the entire group

Mean, median, mode

mean

the average

sum of the scores divided by the number of scores

Population m

Sample M,X

is the score that divides a distribution exactly in half.

Equivalent to the 50th percentile

a number or numbers that appear the most

is often used more casually to refer to scores with relatively high frequencies

variability

provides a quantitative measure ofthe degree to which scores in a distribution are spread out or clustered together

usually defined by terms of distance

measures how well an individual score represent the entire distribution

is the range covered by the middle 50% of the distribution

it allows you to avoid teh excessive influence of one or two extreme scores

Measures the variabiltiy

I.R. = Q3 - Q1

Range: max - min score

Sum of squares: S(x-mean)² 

                     N

Variance:    √S(x-mean)² 

                     N

is half of the interquartile range

S-I. R= Q3-Q1

         2

is the distance from the mean

Deviation score= X - μ

= the mean squared deviation

Variance is the mean of the squared deviation scores

is the sum of the squared deviation scores

Variance= Mean squared deviation= sum of squared deviations

                                              number of scores

SS=Σ(x-μ)²

= √SS

     N

=SS

  N

σ=√σ² =√SS

              N

σ²=SS

     N

σ²=Σ (X-μ)²

   N

s²= SS

       n-1

s²= Σ(X-M)²

      n-1

s=√s²= √SS

               n-1

degrees of freedom

df for sample variance are defined as df= n-1

the degrees fo freedom determine the number of scores in the sample that are independent and free to vary

specifies the precise location each X value withing a distribution

(+ or -) signifies whether the score is above the mean or below the mean

Specifies the distance from the mean by counting the number of standard deviation between X and μ

is composed of scores that have been transformed to create predetermined values for the μ and σ.

Standardized distribution are used to make dissimilar distributions comparable