Honors Biology Mr. Luis A. Velázquez Qualitative vs. Quantitative Data Honors Biology Mr. Luis A. Velázquez

Qualitative vs. Quantitative Data Quantitative data is information about quantities; that is, information that can be measured and written down with numbers. Qualitative data is information about qualities; information that can't actually be measured.

Qualitative vs. Quantitative Data The age of your car.   The number of hairs on your knuckle.   The softness of a cat.   The color of the sky.   The number of pennies in your pocket. (Quantitative.) (Quantitative.) (Qualitative.) (Qualitative.) (Quantitative.)

Categorical Data This is data that can be organized into mutually exclusive categories. If we look at a bunch of bananas and they're all either green, brown, yellow or blue, then we could use the categories "green," "brown," "yellow" and "blue" to record our data. Categorical data is usually qualitative. However, quantitative data can also be put into categories.

Raw Data Unanalyzed data; data not yet subjected to analysis. Raw data is never is use in a graph. Also known as primary data.

Raw Data according to statistics, the details given by investigator or collected from sources are known as raw data. In other words its the first hand information undergone no mathematical or statistical treatment also called as raw data.

Results vs. Conclusion Conclusion and Results are two terms used in thesis writing and surveys or experiments respectively. Conclusion forms the end part of a thesis or a dissertation. Results form the end part of a survey or a chemical experiment. This is one of the main differences between conclusion and results. Read more: http://www.differencebetween.com/difference-between-conclusion-and-vs-results/#ixzz2fMSoh3ER

Conclusion aims at the briefing of the research findings of the researcher. It should be short and concise. It should contain concise and short paragraphs. A conclusion should not contain long paragraphs. Results can be statistical in composition and sometimes descriptive too. If they are descriptive in nature then they can contain long paragraphs. Read more: http://www.differencebetween.com/difference-between-conclusion-and-vs-results/#ixzz2fMTS2L5q

Null Hypothesis The simplistic definition of the null is as the opposite of the alternative hypothesis. The null hypothesis (H0) is a hypothesis which the researcher tries to disprove, reject or nullify. The 'null' often refers to the common view of something. The alternative hypothesis is what the researcher really thinks is the cause of a phenomenon. Read more: http://explorable.com/null-hypothesis

An experiment conclusion always refers to the null, rejecting or accepting H0 rather than H1. Despite this, many researchers neglect the null hypothesis when testing hypotheses, which is poor practice and can have adverse effects. Read more: http://explorable.com/null-hypothesis

A researcher may postulate a hypothesis: H1: Tomato plants exhibit a higher rate of growth when planted in compost rather than in soil. And a null hypothesis: H0: Tomato plants do not exhibit a higher rate of growth when planted in compost rather than soil.