Question

• Describe the difference between descriptive and inferential statistics

• Identify the level of measurement of a variety of variables

• Describe the difference between measures of central tendency and variability

• Explain the difference between correlation and causation

• Describe measurement validity and reliability and name the statistics used to test them

• Explain the difference between a population and a sample

• Describe what correlations are and how they are used

only answer if you know these. Thanks.

Answer 1

Step(1)-

Difference between Descriptive Statistics and Inferential Statistics

	Descriptive Statistics	Inferential Statistics
Meaning	Descriptive Statistics is that branch of statistics that is bothered with describing the population in which we are intrested	Inferential Statistics may be a variety of statistics, that focuses on drawing conclusions regarding the population, on the idea of sample analysis and observation.
Purpose	Organize, analyze and present the data in an exceedingly purposeful manner.	Compares, take a look at and predicts data.
Result in form of	Charts, Graphs and Tables
Use	To describe a state of affairs.	To explain the possibilities of occurrence of an event
Work	It explains the data, that is already summarize sample	It attempts to reach the conclusion to learn about the population, that extends beyond the data available.

Step(2)-

Variables has four  measurement level : Nominal, Ordinal, Interval, or Ratio.

Nominal scale is a scale like one simply names or categorizes responses. Gender, handedness, favorite color, and religion are examples of variables measured on a nominal scale.

ordinal scale is where order and ranking of the responses is very important like strongly agree,agree disagree, strongly disagree and Low, medium,high.

Interval scale is defined as a quantitative measurement scale where the difference between 2 variables is meaningful.e.g.  the difference between 40 and 30 degrees is a measurable 10 degrees

Ratio scale is a type of variable measurement scale which is quantitative in nature like weight,height, length etc.

Step(3)-

Measure of central tendency is the mid value in the population on which all the data is congregated around it it is a  summary statistic that represents the center point or typical value of a data set. . in statistics three main central of statistics are mean ,median mode.

Measures of variability is the measurment of  spread in a set of data around central tendency. The most common measures of variability are the range, the interquartile range (IQR), variance, and standard deviation.

Step(4)-

Correlation is generally the linear relationship between two events or variables simply indicates that a relationship exists and Causation is the relationship between cause and effect. So, when a cause results in an effect, that's a causation. In other words, whereas causation is more specific and says that one event actually causes the other. Causation  is commonly misunderstood and misused by people in the mistaken belief that because the data shows a correlation that there is necessarily an underlying causal relationship .

Step(5)-

Reliability examines whether you are getting consistent information. Validity examines whether the information you are getting measures what you think it measures. Cronbach's alpha is the most common measure of internal consistency ("reliability").  validity testes using Pearson Product Moment Correlations.

Step(6)-

population is the group of people (anything) to whom you intend to generalize the results of your study. sample is always be a subset of your population. In other word we can say  population includes all of the elements from a set of data and the sample consists one or more observations drawn from the population.

Step(7)-

Correlation, is a measure of the strength and direction of association that exists between two variables. correlation generates a coefficient called the correlation coefficient, denoted as r. A correlation attempts to draw a line of best fit through the data of two variables, and the correlation coefficient, r, indicates how far away all these data points are to this line of best fit (i.e., how well the data points fit this new model/line of best fit). Its value can range from -1 for a perfect negative linear relationship to +1 for a perfect positive linear relationship. A value of 0 (zero) indicates no relationship between two variables.e.g. shoe sizes go up in (almost) perfect correlation with foot length.