Qualitative vs Quantitative Data Honors Biology Mr Luis

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. (Quantitative. ) • The number of hairs on your knuckle. (Quantitative. ) • The softness of a cat. (Qualitative. ) • The color of the sky. (Qualitative. ) • The number of pennies in your pocket. (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/differencebetween-conclusion-and-vs-results/#ixzz 2 f. MSoh 3 ER

• 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/differencebetween-conclusion-and-vs-results/#ixzz 2 f. MTS 2 L 5 q

Null Hypothesis • The simplistic definition of the null is as the opposite of the alternative hypothesis. • The null hypothesis (H 0) 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 H 0 rather than H 1. • 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: • H 1: Tomato plants exhibit a higher rate of growth when planted in compost rather than in soil. And a null hypothesis: • H 0: Tomato plants do not exhibit a higher rate of growth when planted in compost rather than soil.