Observations and Inferences Scientists use observations and inferences to understand phenomena in the real world

What is an “observation”? Basic definition. 5. Scientists (and students) test their ideas against their observations. 4. Scientists (and students) often use tools to enhance their observations. 3. Scientists (and students) make and record observations with care. 2. Scientists (and students) use observations to describe what they experience. 1. An observation is something that you see, feel, hear, taste or touch.

What is an “inference”? An inference is: To reach a conclusion based on evidence. Using observations and logic to draw out meaning from data. Short contrast: An observation is what you see, feel, taste, or smell DIRECTLY. An inference is what you think is happening or causing something based on REASONING.

Inference: An example:

Make a list of everything that you notice about this image. Circle the statements that you think represent OBSERVATIONS. Underline the statements that you think represent INFERENCES. Image #1:

Observations? Inferences? Image #2

Image #3: Make a chart with columns for: Observations and Inferences for this image. Tools enhance observations

Image #3B A “tool” has been added/used to help interpret the image. How has this “tool” changed the perception of the image? Observation Tool

Image #4a How can small details in observations or perceptions make a difference in inferences? Using both observations and inferences Compare/contrast these two images. Multiple observation value

Image 4b With two additional images added to the set, how have these CHANGED your perception of what they are showing?

Image 4c. How have these two additional images added to your understanding of what is being shown? What type of prediction can you make about the last two images? What is your prediction?

Image 4d. What additional observations and inferences can be made about the entire set of images? How does this show that multiple observations and inferences lead to better understanding? Multiple observation value

Image 5a How can the context or environment in which observations and inferences are made affect them or their interpretation? A BC DE Give a BRIEF description of each lettered image based on your observations. Contextual Importance

Image 5b. A BC DE Context change and inference. What description would you place on Image E when included with the second set of images? How can the context in which observations or inferences are made affect them?

Observations? Inferences?What should the caption say?

The Scientific Process: 1. Phenomenon occurs and is “noticed”. 2. Observations are made about phenomenon. 3. Inferences are made about phenomenon. 4. Evaluation of observations/inferences leads to other explorations/experimentation to better understand the phenomenon.

a. Phenomenon— noticing similar phenomena b. Data collection--using senses: see, hear, touch, taste, smell, etc. Involves direct interaction or collection of information from phenomenon. Collected data can be either qualitative or quantitative. Some collected data may require the use of measurement tools. c. Data grouping— assigning categories to data that is collected based on similar observed properties. d. Context—the environment in which the phenomenon occurred (Where, when, how, and why data is collected.) e. Analysis—BOTH: Looking for accuracy (is data providing REAL info on phenomenon) and precision (is data consistent with other data.) Also, allows one to notice patterns and limits in the collected data. Observations: 5 types

Inferences: 5 types a. Data interpretation— Compare and contrast the data with something else (Infer that data is similar or different from other observed phenomena). b. Pattern recognition— statement of possible patterns/trends in data with goal of making a prediction about future data from this or other phenomena. Predictions may be generalized as either a scientific law or theory. c. Data limits— statement about how limits in the observations or data may be affecting their interpretation or the accurate recognition of a pattern. d. Theory comparison— comparing data with stated hypothesis or scientific theory that may be causal or correlated with phenomenon. e. Meaning interpretation— statement of what observations or data mean in terms of other phenomena or causal relationships in the real world..

Observations: Typical codes/categories P = PhenomenonNotice another/new phenomenon related to the original one studied Qu = Qualitative DataDirect observations NOT involving numerical values/scales Qa= Quantitative DataDirect observations involving numerical measurements or comparison to a numerical scale. C= Categorization of Data Observation of possible grouping or categories of data related to the observed phenomenon. T= Contextualization of Data Observations related to the context/ environment/situation in which the phenomenon occurred and effects on the data collected. A= Analysis of DataObservations of accuracy (Are the data/observations measuring the “real” phenomenon?) and the precision (Are the data and observations consistent with each other?) of collected data. Noticing that data follow an obvious pattern or that there may be limits in the data due to missing pieces or inaccuracies.

Inferences: Typical codes/categories I= Interpretation of DataComparing data/observations with other known phenomena (Inferring that data or phenomenon is similar/different from some other known phenomenon or data.) P= Patterns/PredictionsStating possible patterns/trends indicated by data/observations with goal of using them to make future predictions about similar phenomena L= LimitationsStating how irregularities in data/observations or lack of enough data/observations may be affecting recognition of patterns/trends. C= ComparisonComparing data/observations from phenomenon with a stated hypothesis or known scientific theory. (Infer that phenomenon may be causal or correlated to this hypothesis/theory.) M= MeaningStating how the phenomenon and/or data/observations may have meaning in explanations about the nature of the phenomenon and its causes/relationships in the real world.

Qualitative Observations: Examples Qualitative: (mostly Physical properties) * Color * Luster (surface description) * State of matter (solid, liquid, etc.) * Shape * Texture * Non-numerical comparisons (looks like, feels like, etc.) * Descriptions * Reactions to certain stimuli or chemicals

Quantitative Observations: Examples Quantitative: (can all be measured with numerical scales) * Size or Distance: length, width, height in centimeters, meters, etc. * Mass or Weight: grams, pounds, etc. * Volume: cubic centimeters, or as liters, etc. * Density –how closely packed things are inside (mass per volume) * Temperature—degrees Celsius * Pressure—bars or pounds per square inch * Hardness—Mohs scale * Velocity/speed (miles per hour, etc.) * Acceleration—is it speeding up or slowing down? * Light refraction, reflection, (angles) or transmission(intensity in candles or lux) * Radioactivity—does it emit particles or waves? (measured in rads, etc.) * Permeability (how fast liquids travel through) * Electrical Resistance or Conductivity (Ohms, etc.) * Sound: intensity (decibels) or frequency (Hertz—vibrations per second) * Stress/strain: (how much it stretches or bends before breaking) * Rate of physical change: change of state—melting, freezing, etc. (time) * Rate of chemical change: crystallization, chemical reaction, etc. (time)

Three major aspects of science: A body of knowledge A set of methods/processes A way of knowing 1.Definition of observation 2.Examples of scientific observations 3.Examples of inference 1.Making observations 2.Recording observations 3.Using observations to justify conclusions 4.Using tools to enhance observations 5.Distinguishing between observation and inference. 1.Scientific knowledge is partly based on observation and inference. 2.Observations are used to test scientific laws. 3.Scientists use technology to enhance/ extend their observations.