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The validity of a experiment is directly affected by its construction and execution.  Attention to Experimental Design is extremely important.

At IQ Sciences, we meticulously and professionally organize the experiment properly to ensure that the right type of data, and enough of it, is available to answer the questions of interest as clearly and efficiently as possible. This process is part of IQ Sciences Experimental Design.

The specific questions that the experiment is intended to answer must be clearly identified before carrying out the experiment. We at IQ Sciences identify known or expected sources of variability in the experimental units to reduce the effect of these sources of variability on the answers to questions of interest. That is, we design the experiment in order to improve the precision of our answers.

Our Experimental Design is the process of planning a study to meet the specified objectives. Planning an experiment properly is very important in order to ensure that the right type of data and a sufficient sample size and power are available to answer the research questions of interest as clearly and efficiently as possible.

At IQ Sciences we offer the following Experimental Design services and  various stages of development for the marketing of your  new product concepts.

Evaluate the need for the marketing the product

Cost, regulatory, target market and determining competition

Experimental Development

Feasibility Study

Establish Specifications

Establish SOP's and Quality Control.

Design Control

Validation 

At IQ Sciences, we pride ourselves on Quality Service, close effective communication, and developing a long lasting relationship with our clients.

As a small business, IQ Sciences works with you to meet your goals at an affordable price that is mutually reasonable and within your budget.

Please contact us for more information.

Analysis of Varience

Balanced Design

Blocking

Center Points

Comparative Design

Crossed Factors

Design Matrix

Effect

Experimental Unit

Factors

Fixed Effect

Interaction

Model

Nested Factors

Random Effect

Random Error

Replication

Resolution

Response

Screening Design

Treatment

Variance Components