Understanding Statistics - Research Methods Part-1
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# Understanding Statistics - Research Methods Part-1

### This page covers the basic concepts of statistics that are good to know.

Source

Udacity : Intro to Descriptive Statistics

### Population

All the factories that participated in a quality test is known as Population.

### Sample

From a factory 100 items were randomly picked for quality checks, these 100 items are an example of Sample.

### Statistic

A group of 100 workers is interviewed and the interviewer finds that these workers work an average of 10 hours per day.

This average is an example of Statistic.

### Parameter

A number that describes population is called a parameter.

### Sampling Error

The difference between the sample and population averages is known as sampling error.

### Sample not representing/covering data

If we want to know the number of hours that factory worker works per week,

but we take the data from only one factory,

then the sample may not be representative of the factory workers working in other factories.

### Construct

A construct is a variable that is not directly observable or measurable.

Example Of Construct:

1. How angry my dog is after I step on its tail by mistake!

2. Someone’s personality.

3. Aggression

Things that can be measured are not constructs:

Examples:

Price of a watch.

Height of a building.

### Operational Definition

Is a way of turning constructs into variables we can measure.

Is a way of describing a variable in terms of the way we measure it.

Example: Operational definition of aggression is the score on the Aggression Scale.

It’s often too expensive to collect data about an entire population, so we try to learn about the population using a sample. We do this by estimate population parameters using sample statistics. However, we can’t expect our estimates to be exactly accurate when we do this.

So in most research studies:

1. Data from individuals in a sample are used to learn about a population.
2. We expect our best guesses (estimates) of the population parameters to differ from the actual values.
3. We expect our sample statistics will not be exactly equal to the population parameters they are estimating.

### Variable

A variable is a value that may change or differ between individuals in an experiment. Examples:

1. Weather someone is dead or alive.
2. Scores on intelligence test.
3. The number of friends people have on facebook.

### Constant

Constant is a value that will not change.

Example:

1. The moon’s circumference will always have the same value, so it is called a constant.
2. Number of seconds in a minute.
3. The number of hydrogen atoms in a molecule of pure water.

### Hypothesis

Explanation made on the basis of limited evidence as a starting point for further investigation.

Example:

1. The more hours of sleep you get, the better your memory will become.
2. The more people who give an incorrect answer, the more likely you are to give the same incorrect answer.

### Types of variables

A good article on these from statisticshowto : types-of-variables

### Independent variable

a variable that is not affected by anything that you, the researcher, does. Usually plotted on the x-axis.

### Dependent variable

The outcome of an experiment.

As you change the independent variable, you watch what happens to the dependent variable.

### Qualitative variable

A broad category for any variable that can’t be counted (i.e. has no numerical value).

Nominal and ordinal variables fall under this umbrella term.

### Predictor variable

similar in meaning to the independent variable,

But used in regression and in non-experimental studies.

### Extraneous (or Lurking) variables

A “hidden” variable the affects the relationship between the independent and dependent variables.

Provide possible alternative explanations for observed relationships between variables.

Are factors that could influence the relationships we measure between two or more variables.

Should be controlled in an experiment before we can make confident casual statements.

Make it difficult to make casual statements from data from observational studies.

### X Bar Symbol (x̄)

Is the statistical symbol for the sample average.

### Mu Symbol (μ)

Is the statistical symbol for the population average.

### Random Sample

A sample is called random sample in which we select

1. Individuals in such a way that everyone has the same chance of being selected.

2. Individuals in such a way that the selection of one individual has no effect on anyone else’s chances of being selected.

### Convenience sample

A convenience sample is a type of non-probability sampling method where the sample is taken from a group of people easy to contact or to reach.

Example:

Standing at a mall or a grocery store and asking people to answer questions would be an example of a convenience sample.

### Observational Study

In this type of study, we measure or survey members of a sample without trying to affect the members or manipulating the variables.

Here researchers do not impose any kind of treatment or restriction to the group nor do they randomly assign the subjects to a group.

### Experimental Study

In this we experiment and manipulate the environment of the subject to measure the response (dependent) variable.

Evidence provided by the experimental study is considered to be stronger than the observational study.

Part 2