**RESEARCH ERROR**

There are two broad types of error present in all research:

(1)

**SAMPLING ERROR,**or error related to selecting a sample from a population; and
(2)

**NONSAMPLING ERROR, s**uch as**Measurement errors,**

**Data analysis errors,.**

**Measurement error :**Some of the most common measurement errors include:

·
A
poorly designed measurement instrument

·
Asking respondents
the wrong questions or asking questions incorrectly.

It

**is further divided into two****categories:****Random error:**Random error relates to problems where measurements and analyses vary inconsistently from one study to another—

**Systematic error:**systematic error consistently produces incorrect (invalid) results in the same direction, or same context, and is, therefore, predictable.

The cause of systematic errors and eliminate
their influence.

·
Faulty
data collection equipment

·
Untrained
data collection personnel

·
Using only one type
of measurement instead of multiple measures

·
Data
input errors

**SAMPLING ERROR**

Sampling
error find out by an estimate of the difference between observed
and expected measurements and is the foundation of all research interpretation.

Sampling error provides an indication of how close the data from a
sample are to the population mean. A low sampling error indicates that there
is less variability or range in the sampling distribution.

A theoretical sampling
distribution is the set of all possible samples of a given size. This
distribution of values is described by a bell-shaped curve or

**normal curve**(also known as a**Gaussian distribution,**after Karl F. Gauss, a German mathematician and astronomer who used the concept to analyze observational errors).
There are two important terms related to computing
errors due to sampling:

standard error (designated
as SE) and

sampling error,

*which is also referred to as margin of error or confidence interval (designated as**se*or*m,*or*Cl).**(1)*

**Standard error**relates to the population and how samples relate to that population. Standard error is closely related to sample size—as sample size increases, the standard error decreases.

**Confidence Level and**

**Confidence Interval**

Sampling
error involves two concepts:

Confidence
Level

and

Confidence
Interval.

**The confidence level**indicates a degree of certainty (as a percentage) that that the results of a study fall within a given range of values. Typical confidence levels are

*95%*and

*99%.*

The
confi

*dence interval is*a plus-or-minus percentage that is a range within the confidence level. For example, if a*5%*confidence interval is used, and*50%*of the sample gives a particular answer for a question, the actual result for that question falls between*45%*and*55% (50 ± 5).*
In every normal distribution,

**the standard deviation**defines a standard unit of distance from the mean of the distribution to the outer limits of the distribution. These standard deviation interval units (z-values) are used in establishing the confidence interval that is accepted in a research project. In addition, the standard deviation units indicate the amount of standard error. For example, using a confidence level of + 1 or –1 standard deviation unit-1 standard error—says that the probability is that*68%*of the samples selected from the population will produce estimates within that distance from the population value (1 standard deviation unit; see Figure*4.3).***Computing Sampling Error**

There are several ways to compute sampling error, but no single method
is appropriate for all sample types or all situations.

**Sampling error is an important concept in all research areas because it provides an indication of**the degree of accuracy of the research, provides some type of explanation about error.

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